HOW to IMMEDIATELY DISPROVE RUSSIAN HACKING

COURTESY of NSA WHISTLEBLOWER BILL BINNEY
http://www.computerweekly.com/feature/Interview-the-original-NSA-whistleblower
https://www.reddit.com/r/IAmA/comments/3sf8xx/im_bill_binney_former_nsa_tech_director_worked/
http://www.washingtonsblog.com/2016/12/creator-nsas-global-surveillance-system-calls-b-s-russian-hacking-report.html
http://www.washingtonsblog.com/2017/11/70011.html
How to Instantly Prove or Disprove Russian Hacking
by WashingtonsBlog  /  November 14, 2017

“It’s newsworthy that CIA head Mike Pompeo recently met with Bill Binney – who designed the NSA’s electronic surveillance system – about potential proof that the DNC emails were leaked rather than hacked. It’s also noteworthy that the usual suspects – Neocon warmongers such as Max Boot – have tried to discredit both Binney and Pompeo. But there’s a huge part of the story that the entire mainstream media is missing. Specifically, Binney says that the NSA has long had in its computers information which can prove exactly who hacked the DNC … or instead prove that the DNC emails were leaked by a Democratic insider. Remember – by way of background – that the NSA basically spies on everyone in America … and stores the data long-term.

After the story of Pompeo’s meeting with Binney broke, Binney told Washington’s Blog:

Here’s what they would have from the programs you list [i.e. NSA’s Fairview, Stormbrew and Blarney spying programs, which Edward Snowden revealed] plus hundreds if not thousands of trace route programs embedded in switches in the US and around the world.

First, from deep packet inspection, they would have the originator and ultimate recipient (IP) of the packets plus packet series 32 bit number identifier and all the housekeeping data showing the network segments/path and time to go though the network.

 

And, of course, the number of packet bits. With this they would know to where and when the data passed. From the data collection, they would have all the data as it existed in the server taken from.  That’s why I originally said if the FBI wanted Hillary’s email, all they have to do is ask NSA for them. All this is done by the Narus collection equipment in real time at line rates (620 mbps [mega bits per second,] for the STA-6400 and 10 gbps [giga bits per second] for the Insight equipment).

 

Binney explained what these numbers mean:  Each Narus Insight device can monitor and record around 1,250,000 emails each second … or more than 39 trillion emails per year. Wired reported in 2006:

Whistle-blower Klein allegedly learned that AT&T was installing Narus boxes in secure, NSA-controlled rooms in switching centers around the country.

Binney told us there are probably 18 or so Narus recording systems throughout the U.S. deployed by the NSA at AT&T facilities, drawing our attention to the following NSA document leaked by Edward Snowden:

And this AT&T graphic:

(Binney has figured out their locations from publicly-available sources. He has also mapped out similar monitoring systems at Verizon facilities.) Binney also sent me hard-to-find company literature for Narus.  Here are some interesting excerpts:

Narus Insight …

  • Provides full visibility into network traffic
  • Analyzes at macro or micro level targeting specific or aggregate full-packet data for forensic analysis

And:

Universal data collection from links, routers, soft switches, IDS/IPS, databases, etc. provides total network view across the world’s largest IP networks.

 

Binney also pointed me towards a couple of network engineering principles that show that figuring out who hacked the emails (or proving they were leaked) is well within NSA’s capabilities. Initially, when data is transmitted online, it is sent using the TCP/IP Packet format.  Put simply, data is not sent in a vacuum, but rather as part of a bundle containing a lot of other information. Here’s the TCP part of the bundle:

And here’s the IP part of the bundle:

So any data analyst can learn a tremendous amount about the source address of the sender, the destination address of the receiver and a boatload of other information by using a “packet sniffer” to  inspect the “packets” of information being sent over the web. Additionally, it’s simple to conduct “traceroute” searches. “Traceroute” is a computer network diagnostic tool for displaying the route and measuring transit delays of packets across an Internet Protocol network. Wired reported in 2006:

Anything that comes through (an internet protocol network), we can record,” says Steve Bannerman, marketing vice president of Narus, a Mountain View, California, company. “We can reconstruct all of their e-mails along with attachments, see what web pages they clicked on, we can reconstruct their (voice over internet protocol) calls.”

So NSA can easily use basic packet sniffers and traceroutes, and see this. Remember, Edward Snowden says the NSA could easily determine who hacked the Democratic National Committee’s emails:

Binney told us:

Snowden is right and the MSM is clueless. Do they have evidence that the Russians downloaded and later forwarded those emails to wikileaks? Seems to me that they need to answer those questions to be sure that their assertion is correct… You can tell from the network log who is going into a site.  I used that on networks that I had.  I looked to see who came into my LAN, where they went, how long they stayed and what they did while in my network…

 

Further, if you needed to, you could trace back approaches through other servers etc. Trace Route and Trace Watch are good examples of monitoring software that help do these things.  Others of course exist … probably the best are in NSA/GCHQ and the other Five Eyes countries.  But, these countries have no monopoly on smart people that could do similar detection software.

He explained:

If it were the Russians, NSA would have a trace route to them and not equivocate on who did it.  It’s like using “Trace Route” to map the path of all the packets on the network.  In the program Treasuremap NSA has hundreds of trace route programs embedded in switches in Europe and hundreds more around the world.  So, this set-up should have detected where the packets went and when they went there.

 

He added:

As Edward Snowden said, once they have the IP’s and/or other signatures of 28/29 [the supposed Russian hacking groups] and DNC/HRC/etc. [i.e. the DNC and Hillary Rodham Clinton], NSA would use Xkeyscore to help trace data passing across the network and show where it went. [Background.]

 

In addition, since Wikileaks is (and has been) a cast iron target for NSA/GCHQ/etc for a number of years there should be no excuse for them missing data going to any one associated with Wikileaks… Too many words means they don’t have clear evidence of how the data got to Wikileaks.

 

And he stressed:

If the idiots in the intelligence community expect us to believe them after all the crap they have told us (like WMD’s in Iraq and “no we don’t collect data on millions or hundreds of millions of Americans”) then they need to give clear proof of what they say. So far, they have failed to prove anything. Which suggests they don’t have proof and just want to war monger the US public into a second cold war with the Russians. After all, there’s lots and lots of money in that for the military-industrial-intelligence-governmental complex of incestuous relationships…

 

If you recall, a few years ago they pointed to a specific building in China that was where hacks on the US were originating. So, let’s see the same from the Russians. They don’t have it. That’s why they don’t show it. They want to swindle us again and again and again. You cannot trust these intelligence agencies period.

 

And he told Newsweek:

U.S. officials “know how many people [beyond the Russians] could have done this but they aren’t telling us anything. All they’re doing is promoting another cold war.”

Binney … compared allegations about Russian hacks to previous U.S. fabrications of intelligence to justify the invasion of Iraq in 2003 and the bombing of North Vietnam in 1964. “This is a big mistake, another WMD or Tonkin Gulf affair that’s being created until they have absolute proof” of Russian complicity in the DNC hacks, he charged during a Newsweek interview. He noted that after the Kremlin denied complicity in the downing of a Korean Airlines flight in 1983, the U.S. “exposed the conversations where [Russian pilots] were ordered to shoot it down.” Obama officials “have the evidence now” of who hacked the DNC, he charged. “So let’s see it, guys.“

 

NSA either doesn’t have solid evidence of Russian hacking of DNC emails – which means the Russians didn’t do it – or those with the power to demand NSA produce the evidence simply haven’t asked the right questions.”

PREVIOUSLY

WARRANTLESS SPOOFING
https://spectrevision.net/2012/07/06/listening-in/
HOW to RUN a TRACEROUTE (cont.)
https://spectrevision.net/2008/02/18/how-to-run-a-traceroute-cable-cut-conspiracies/
SEXUAL BLACKMAIL in GOVERNMENT
https://spectrevision.net/2016/10/14/blackmail-uber-alles/

PERMISSIONLESS

ZERO KNOWLEDGE TECHNOLOGY
https://blog.zencash.io/releasing-the-zen-white-paper/
https://ethereumclassic.github.io/blog/2016-12-28-zero-knowledge/
https://decentralize.today/five-reasons-zcash-is-the-most-corporate-cryptocoin-youve-ever-seen-a77ac430b0e8
https://bittrex.com/Market/Index?MarketName=BTC-ZCL
https://zencash.io/assets/Zen%20White%20Paper.pdf
Zen White Paper
by Robert Viglione, Rolf Versluis, Joshua Yabut, and Jane Lippencott /  May 2017

CONTACT
rob[at]zencash.io, rolf[at]zencash.io, josh[at]zencash.io, & jane[at]zencash.io

ABSTRACT
Zen is an end to-end-encrypted system with zero-knowledge technology over which communications, data, or value can be securely transmitted and stored. It is an integration of revolutionary technologies that create a system over which innovation can accelerate by combining three functions that are traditionally done separately: 1) transactions 2) communication, and 3) competitive governance. This is done in a secure and anonymous manner, using a worldwide distributed blockchain and computing infrastructure. The system integrates multiple best-in-class technologies to form an open platform for permissionless innovation that can evolve with user preferences.

PURPOSE
We live in a hyper-regulated and surveilled world where billions of individuals are deprived of basic human rights, such as property ownership, privacy, free association, and access to information. The technology now exists to solve some of these problems, and Zen’s early implementation will do exactly that. Zen is a collection of products, services, and businesses built around an enabling technology stack employing zero-knowledge proofs and a core set of beliefs. As a distributed blockchain system leveraging the latest censorship-evading techniques, fully encrypted communications, and a social and governance model designed for long term viability, Zen will contribute to the human right to privacy and provide the necessary networking infrastructure for people to securely collaborate and build value within a borderless ecosystem. Our mission is to integrate the latest technologies available post-Satoshi with a decentralized, voluntary, and peaceful set of social structures to improve life for anyone who wants to participate. We believe that this is an idea whose time has come. Zen’s framework is a secure, privacy-oriented infrastructure with a governance system structured to enable participants to collaboratively extend functionality in many dimensions. Opportunities include hosting of individual identification data, selective proof of title for property, decentralized banking services, privacy-preserving p2p/b2b asset exchange, mutual aid societies, p2p insurance, decentralized humanitarian aid mechanisms, or use purely as an anonymous token of value. These functions can be utilized to serve disenfranchised populations currently excluded from vital services such as banking and healthcare due to lack of identification, capital, and secure channels. They can also be leveraged by individuals who desire to take ownership over and monetize their private data, or, for example, by enterprising communities that wish to develop a competitive bidding system on internally generated solar energy. The unique implementations are unbounded, the common link being the belief that decentralization is the engine of moral progress, and that voluntary solutions are the most creative and enduring.

HISTORY
Zen builds on the heritage of the best cryptocurrencies, network architecture, and distributed file sharing systems in existence by incorporating both existing as well as new features to yield a solid foundation designed for long term viability. Just as important as our technology stack, we’re building on the latest ideas in distributed consensus and competitive governance. Some of the foundations of our project come from Bitcoin, Dash, Decred, and Seasteading. Zcash extended Bitcoin with fully anonymous shielded transactions, so that users could choose between normal Bitcoin-like addresses (t-addresses) or shielded addresses resistant to traffic correlation analysis (z-addresses). Then we created Zclassic, a Zcash clone that changed some key parameters our community felt were important: we removed both the 20% Founders’ Reward and the slow start to the money supply. Since launching Zclassic, we’ve formed a vibrant open-source community eager to move the technology forward in a unique direction. Some early accomplishments include developing an open source mining pool application for both Zcash and Zclassic, as well as Windows and Mac wallets. Our team realized that Zclassic could be further extended as a fully encrypted network with an innovative economic and governance model that better aligns with Satoshi’s original vision for a decentralized global community. We view Zclassic as a fundamentally pure open-source, all-volunteer cryptocurrency project, while Zen extends into a platform with internal funding to facilitate a broader set of communications, file-sharing, and economic activities.

ZEN TALK
The Z transactions in ZenCash have the ability to incorporate text-based messages, which are encrypted and included in the blockchain. There is a 1024 character limit for these messages, and they enhance the ability for users to conduct secure commerce. Instead of discussing the transaction in other less-secure channels that may not have the same level of privacy enhancements as Zen, users can communicate via the ZenTalk messages with the other party or parties before and after the shielded transfer takes place with very small z transaction spends. These messages can be sent directly from one z address to another, and they can also be sent to a channel. By generating a z address from the hash of a channel name, users can subscribe to the channel and read anything published by anyone to the channel. For example, the channel #ZenCash announcements would hash to zXXXXXXXXXXXX, allowing any user to send an anonymous message to the channel. Each message would cost a finite amount of ZenCash to send, since it is contained in a z transactions, therefore reducing the amount of non-useful messages on common channels. Official announcements would be signed by private key and would only be displayed if deemed valid. Furthermore, essentially private group messages can be published using z transactions by first creating a complex channel name,and then encrypting the contents of the message with keys only the desired recipients have. ZenTalk messages would be encrypted with algorithms such as AES-256 with Perfect Forward Secrecy (PFS), matching current standards of encryption for secure communication.

ZEN PUB
Zen has the ability to publish documents to the IPFS or GNUnet. This is done by publishing a IPFS or GNUnet address in the text field of a z address. The preferred document publishing system at this time is GNUnet, because it provides the required infrastructure for anonymous publishing and maintains an active database of documents. The system is similarly extensible to IPFS or any other future distributing archival system. By creating an anonymous messaging layer in conjunction with an anonymous publishing layer, ZenPub allows for the creation of truly anonymous publications which can be rapidly distributed to interested readers.

ZEN HIDE
It is possible for regulators in countries hostile to crypto-commerce to block traditional crypto-currencies like Bitcoin and even Zcash. Zen uses Domain Fronting to extend the ability to complete transactions in adversarial network environments, as explained in Blocking-resistant communication through domain fronting abstract: “We describe “domain fronting,” a versatile censorship circumvention technique that hides the remote endpoint of a communication. Domain fronting works at the application layer, using HTTPS, to communicate with a forbidden host while appearing to communicate with some other host, permitted by the censor. The key idea is the use of different domain names at different layers of communication. One domain appears on the “outside” of an HTTPS request–in the DNS request and TLS Server Name Indication, while another domain appears on the “inside”–in the HTTP Host header, invisible to the censor under HTTPS encryption. A censor, unable to distinguish fronted and non-fronted traffic to a domain, must choose between allowing circumvention traffic and blocking the domain entirely, which results in expensive collateral damage. Domain fronting is easy to deploy and use and does not require special cooperation by network intermediaries. We identify a number of hard-to-block web services, such as content delivery networks, that support domain-fronted connections and are useful for censorship circumvention.” The specific implementation of Domain Fronting used by Zen at launch is with a Commercial Content Distribution Network, but as with every aspect of our architecture, flexibility is designed in from the start and the system can extend in many directions as the technology evolves.

APPLICATIONS
Zen is what we consider to be an optimally decentralized open source project, and so we expect applications to be built and contributed to the ecosystem by many parties. Many of these contributions will likely come in voluntary open source fashion, but we expect a robust business community to grow around the platform as well.

GOVERNANCE
Zen is designed with a decentralized governance model incorporating multi-stakeholder empowerment and the flexibility to evolve to optimally suit our community. Fundamentally, our philosophy on governance is that we do not know a priori the best approach, but we have some ideas for how to initialize the system and enable it to evolve with the needs of the community. We believe in governance as a service (GaaS) and aim to efficiently provide value to our direct stakeholders, the broader community, and the world. ”Any industry that delivers poor service for a high price deserves to be disrupted” (Quirk, 2017), governance being a consummate example. In solidarity with other projects and ideas taking root around the world, we reject forced centralization and embrace voluntaryism. Rather than entrusting a minority of the people with power, we believe that all people have the right to be trusted with freedom. The core philosophy of our governance model is that decentralization of power maximizes inclusion and creativity. Practical implementations must recognize that pooling resources and effort provides synergies that should be optimally balanced against full decentralization; optimal points being state and time-varying, best determined through voluntary participation and secession. Importantly, we are implementing a system where competing DAOs can emerge to share resources or even completely subsume less efficient or unpopular versions. There should be no one-size-fits-all structure invariant across environment, function, culture, or time; rather, structures should be fluid, suited to specific problems, and flexible to scale when working and fade when failing relative to alternatives. Such a system of systems would dynamically evolve in such a way that it is antifragile to competitive feedback. Our objective governance state will balance decentralization, implementation efficiency, separation of powers, broad stakeholder empowerment, and evolutionary flexibility. This initial state will be the result of at least a 12- to 18-month R&D effort into game theoretic, political science, and economics research into optimal voting mechanisms coupled with feedback from multiple testnet implementations. The project will be one of our first funded efforts with final deliverables including a comprehensive research report and operational code integrated into the Zen network. Within 6 months of governance implementation we expect to have leadership teams in operation from our first full and open election.

OPTIMAL DECENTRALIZATION
By decentralization we mean that everyone has an equal opportunity to participate, that we are fully inclusive, and that decision-making authority is maximally diffuse such that the system is resistant to capture. Theoretical maximum decentralization means that every individual retains authority to equally influence decision-making; this is difficult to implement in practice when pooling resources to collaborate on a common system. Even if implemented in such a pure fashion, individual decisions naturally pool for collaboration efficiency and resources accumulate to certain stakeholders at unequal rates. We cannot stop these natural forces, nor is there reason to categorically deem them harmful in every instance. What we can do is to design the system such that all participation is voluntary, that decision-making power over resource allocation is balanced across a broad cross-section of stakeholder types, and that a credible mechanism exists to evolve with feedback. A structure infused with flexibility is more important than initially designing the best system to suit all circumstances, especially since we are creating a movement so expansive that predicting all developments is essentially impossible. Implementation efficiency is also a big concern for decentralized organizations. Pure decentralization could suffer decision-making paralysis, voter apathy, or delusions of the herd at the extrema. This is why we initially shy away from a system of pure democracy for all decision-making, and are taking the time to research competing models and test them under varying conditions of stress. Our proposed system of free and open competition for DAOs is designed to encourage groups of high-performing functional area experts and professionals to propose their leadership in specialized domains so that our system-wide efficiency in converting resources to higher-value end products or services is continually evolving to suit user needs and demands.

CHECKS & BALANCES
A key lesson learned from human history is that powers are best separated and competing power clusters should provide some equilibrium state of checks and balances. The balancing should be resilient to unchecked growth in any single power cluster such that the entire system succumbs to capture. To initially prevent this condition, Zen is launching with a Core Team in control of 3.5% of block reward funding, and an initial DAO comprised of industry leaders controlling 5% of resources. In addition, our objective state to be implemented after the 12- to 18-month R&D and test phase will include a hybrid type of multi-stakeholder voting so that a wide cross-section of the community retains power to influence decisions and resource allocations. Every aspect of our governance structure will ultimately be subject to competitive feedback and change. We are taking an evolutionary approach that starts with a simple model that will grow with the community.

DAO : INFRASTRUCTURE, PROPOSALS, and VOTING
The Zen system will have at least one DAO funded by a portion of the mining rewards, and governed by a voting system that brings stakeholders together. This system of governance helps ensure that implementation of changes, improvements, and integrations minimizes contention and reduces the chance that a disagreement leads to a fork in the project. As we unroll our broader governance plan derived from rigorous R&D and testing, the goal is to open the governance landscape to full competition; this means that we could see multiple competing DAOs emerge with different teams working on different problems. Each DAO would emerge with its own proposed structure, processes, and goals, which ensures these attributes are evolving through competition and the wrong initial organizational decisions do not perpetuate. Our DAOs will be responsible for building, maintaining, and improving the infrastructure that keeps the system going. It is also responsible for implementing changes to the Zen software applications, and is flexible enough to accommodate other community priorities,such as community outreach, marketing, training, etc.

As the Zen system grows in popularity, the support structures for users, miners, Secure Node operators, and ecosystem partners will need to grow and scale as well. The DAO structures will have funds, allocated through projects and proposals, with which to assist in the growth and support. The community is encouraged to participate in contributing to Zen in all different ways. The DAOs are responsible for coordinating the community contributions, and have funds to assist in offsetting expenses incurred by the community. One of the purposes of proposals is to repay community members for their expenses in supporting the system. At launch, Zen will have one DAO staffed with respected professionals that span relevant industries. When the governance plan is ready for implementation, this DAO will be one proposed grouping subject to market competition for others who might wish to stand up their own governance structures; the broad community will make that decision.

COMPETITION
Our unique innovation to the cryptocurrency community is our fully competitive and evolutionary governance model to empower a broad cross-section of stakeholders in an environment of optimal decentralization. Bitcoin created the original breakthrough in distributed consensus, but other projects have since taken that further with various voting mechanisms. These projects range from Dash with its simple proposal submission and community voting model all the way to Decred with its embedded community governance; each has contributed positively to the evolution of decentralized consensus, but Zen takes this to the next level by relaxing additional constraints such that our system is set to evolve over time through perpetual competition between providers of governance services within the ecosystem. We are implementing an autonomous system that will change with feedback and trial-and-error innovations in how decentralized systems organize to solve specific problems. In this sense, we believe Zen is groundbreaking in social technology, pioneering a system that has never been attempted at scale. From a broader perspective, Zen competes with incumbent currencies and banking systems, as well as emergent FinTech startups with particular advantage in providing services to the disenfranchised. We choose to make our contribution to this innovative, social welfare oriented space by providing enhanced privacy and security. As a secure messaging and distributed data archival system, we compete with other services, such as Signal, Telegram, and the Tor Project. There are also an infinite number of potential projects that can be built on the Zen platform, increasing our competitiveness exponentially. We view competition as an enabler of healthy processes of growth and therefore welcome maximum competition. We’d rather live in a world with fierce competitors forcing us to accelerate our own innovations than a static world devoid of progress. We hope that Zen adds positively to human welfare by integrating great technologies and communities, morphing governance into a competitive service, and enabling anyone in the world to participate in our system of permissionless, collaborative, and decentralized innovation. We also view incumbents and future startups in this space as potential partners and allies instead of winner-takes-all competitors.

FUTURE
Forecasting is a challenging exercise, but we see a bright future for Zen and the peaceful and productive ecosystem we’re building. We believe that the decentralized, fully inclusive, voluntary, and flexible organization we’re creating will be seen as obviously superior in the future compared to the static, centralized, one-size-fits all versions perpetuated in the 20th century. The advent of cryptography, voluntaryist philosophy, and blockchain technology make such a thing possible, and we believe many people already do, and will, share our vision for a better world; especially when they see how we can accelerate innovation and improve human welfare by empowering everyone to express their values. The next one to two years will see this vision come to fruition in our early organization by executing our Roadmap. There will certainly be challenges along the way, but flexibility and peaceful cooperation consistently overcomes seemingly insurmountable issues.”

PSYCHOMETRICS

PSYCHOGRAPHIC MICROTARGETING
http://www.stopdatamining.me/opt-out-list/
https://www.propublica.org/article/facebook-lets-advertisers-exclude-users-by-race
https://mathbabe.org/2016/08/11/donald-trump-is-like-a-biased-machine-learning-algorithm/
https://www.technologyreview.com/s/601214/how-political-candidates-know-if-youre-neurotic/
https://www.bloomberg.com/politics/features/2015-11-12/is-the-republican-party-s-killer-data-app-for-real-
https://www.theguardian.com/us-news/2015/dec/11/senator-ted-cruz-president-campaign-facebook-user-data
http://www.slate.com/articles/news_and_politics/dispatches/2005/09/you_cant_handle_the_truth.single.html
https://medium.com/startup-grind/how-the-trump-campaign-built-an-identity-database-and-used-facebook-ads-to-win-the-election-4ff7d24269ac
https://antidotezine.com/2017/01/22/trump-knows-you/
Trump Knows You Better Than You Know Yourself
by   /  22/01/2017

AntiNote: The following is an unauthorized translation of a December 2016 article that caused quite a stir in the German-language press. Das Magazin (Zurich) occupies a respected position within the German-language cultural and literary media landscape, functionally similar to (though perhaps not quite as prominent as) The New Yorker, and this work by investigative reporters Hannes Grassegger and Mikael Krogerus got a lot of attention—and generated some controversy, for apparently having scooped the English-language media with sensational observations about 2016’s most sensational story, the campaign and electoral victory [of Donald Trump]. Perhaps for this reason, the article has not appeared in translation in (or even had its investigative threads taken up by) English-language media outlets, even after nearly two months. Antidote presents, therefore, our own preemptive translation to fill this gap. We trust the skill of the reporters who wrote it and the veracity of their claims (which are verifiable by anyone with a search engine—we have embedded links where appropriate), and we question why this particular synthesis of public information is not being made available to non-German-speaking readers by outlets with more reach and respectability than us dirty DIY dicks. On the occasion of this article’s authorized wider release in English, should that come to pass, we will consider removing this post if we are asked nicely. Until then: Enjoy!”

the LOW-INFORMATION VOTER
https://www.nytimes.com/2016/10/27/opinion/campaign-stops/the-great-democratic-inversion.html
https://www.washingtonpost.com/news/monkey-cage/wp/2016/11/07/low-information-voters-are-a-crucial-part-of-trumps-support/
https://pdpecho.com/2016/11/14/does-eu-data-protection-law-apply-to-the-political-profilers-targeting-us-voters/
https://www.dasmagazin.ch/2016/12/03/ich-habe-nur-gezeigt-dass-es-die-bombe-gibt/
“I just showed that the bomb was there”
by Hannes Grassegger & Mikael Krogerus  /  translated by Antidote  /  3 December 2016

“On November 9th, around 8:30 in the morning, Michal Kosinski awoke in his hotel room in Zurich. The 34-year-old had traveled here to give a presentation to the Risk Center at the ETH [Eidgenössische Technische Hochschule or Federal Institute of Technology, Zurich] at a conference on the dangers of Big Data and the so-called digital revolution. Kosinski gives such presentations all over the world. He is a leading expert on psychometrics, a data-driven offshoot of psychology. Turning on the television this morning in Zurich, he saw that the bomb had gone off: defying the predictions of nearly every leading statistician, Donald J. Trump had been elected president of the United States of America. Kosinski watched Trump’s victory celebration and the remaining election returns for a long while. He suspected that his research could have had something to do with the result. Then he took a deep breath and turned off the television. On the same day, a little-known British company headquartered in London issued a press release: “We are thrilled that our revolutionary approach to data-driven communications played such an integral part in president-elect Donald Trump’s extraordinary win,” Alexander James Ashburner Nix is quoted as saying. Nix is British, 41 years old, and CEO of Cambridge Analytica. He only appears in public in a tailored suit and designer eyeglasses, his slightly wavy blond hair combed back. The meditative Kosinski, the well-groomed Nix, the widely grinning Trump—one made this digital upheaval possible, one carried it out, and one rode it to power.

How dangerous is Big Data?
Anyone who didn’t spend the last five years on the moon has heard the term Big Data. The emergence of Big Data has meant that everything we do, online or off-, leaves digital traces. Every purchase with a card, every Google search, every movement with a cellphone in your pocket, every “like” gets stored. Especially every “like.” For a while it wasn’t entirely clear what any of this data would be good for, other than showing us ads for blood pressure medication in our Facebook feeds after we google “high blood pressure.” It also wasn’t entirely clear whether or in what ways Big Data would be a threat or a boon to humanity. Since November 9th, 2016, we know the answer. Because one and the same company was behind Trump’s online ad campaigns and late 2016’s other shocker, the Brexit “Leave” campaign: Cambridge Analytica, with its CEO Alexander Nix. Anyone who wants to understand the outcome of the US elections—and what could be coming up in Europe in the near future—must begin with a remarkable incident at the University of Cambridge in 2014, in Kosinski’s department of psychometrics.

Psychometrics, sometimes also known as psychography, is a scientific attempt to “measure” the personality of a person. The so-called Ocean Method has become the standard approach. Two psychologists were able to demonstrate in the 1980s that the character profile of a person can be measured and expressed in five dimensions, the Big Five: Openness (how open are you to new experiences?), Conscientiousness (how much of a perfectionist are you?), Extroversion (how sociable are you?), Agreeableness (how considerate and cooperative are you?), and Neuroticism (how sensitive/vulnerable are you?). With these five dimensions (O.C.E.A.N.), you can determine fairly precisely what kind of person you are dealing with—her needs and fears as well as how she will generally behave. For a long time, however, the problem was data collection, because to produce such a character profile meant asking subjects to fill out a complicated survey asking quite personal questions. Then came the internet. And Facebook. And Kosinski.

A new life began in 2008 for the Warsaw-born student Michal Kosinski when he was accepted to the prestigious University of Cambridge in England to work in the Cavendish Laboratory at the Psychometrics Center, the first-ever psychometrics laboratory. With a fellow student, Kosinski created a small app for Facebook (the social media site was more straightforward then than it is now) called MyPersonality. With MyPersonality, you could answer a handful of questions from the Ocean survey (“Are you easily irritated?” – “Are you inclined to criticize others?”) and receive a rating, or a “Personality Profile” consisting of traits defined by the Ocean method. The researchers, in turn, got your personal data. Instead of a couple dozen friends participating, as initially expected, first hundreds, then thousands, then millions of people had bared their souls. Suddenly the two doctoral students had access to the then-largest psychological data set ever produced.

The process that Kosinski and his colleagues developed over the years that followed is actually quite simple. First surveys are distributed to test subjects—this is the online quiz. From the subjects’ responses, their personal Ocean traits are calculated. Then Kosinski’s team would compile every other possible online data point of a test subject—what they’ve liked, shared, or posted on Facebook; gender, age, and location. Thus the researchers began to find correlations, and began to see that amazingly reliable conclusions could be drawn about a person by observing their online behavior. For example, men who “like” the cosmetics brand MAC are, to a high degree of probability, gay. One of the best indicators of heterosexuality is liking Wu-Tang Clan. People who follow Lady Gaga, furthermore, are most probably extroverted. Someone who likes philosophy is more likely introverted.

Kosinski and his team continued, tirelessly refining their models. In 2012, Kosinski demonstrated that from a mere 68 Facebook likes, a lot about a user could be reliably predicted: skin color (95% certainty), sexual orientation (88% certainty), Democrat or Republican (85%). But there’s more: level of intellect; religious affiliation; alcohol-, cigarette-, and drug use could all be calculated. Even whether or not your parents stayed together until you were 21 could be teased out of the data. How good a model is, however, depends on how well it can predict the way a test subject will answer certain further questions. Kosinski charged ahead. Soon, with a mere ten “likes” as input his model could appraise a person’s character better than an average coworker. With seventy, it could “know” a subject better than a friend; with 150 likes, better than their parents. With 300 likes, Kosinski’s machine could predict a subject’s behavior better than their partner. With even more likes it could exceed what a person thinks they know about themselves. The day he published these findings, Kosinski received two phonecalls. One was a threat to sue, the other a job offer. Both were from Facebook.

Only Visible to Friends
In the meantime, Facebook has introduced the differentiation between public and private posts. In “private” mode, only one’s own friends can see what one likes. This is still no obstacle for data-collectors: while Kosinski always requests the consent of the Facebook users he tests, many online quizzes these days demand access to private information as a precondition to taking a personality test. (Anyone who is not overly concerned about their private information and who wants to get assessed according to their Facebook likes can do so at Kosinski’s website, and then compare the results to those of a “classic” Ocean survey here). It’s not just about likes on Facebook. Kosinski and his team have in the meantime figured out how to sort people according to Ocean criteria based only on their profile pictures. Or according to the number of their social media contacts (this is a good indicator of extroversion). But we also betray information about ourselves when we are offline. Motion sensors can show, for example, how fast we are moving a smartphone around or how far we are traveling (correlates with emotional instability). A smartphone, Kosinski found, is in itself a powerful psychological survey that we, consciously or unconsciously, are constantly filling out.

Above all, though—and this is important to understand—it also works another way: using all this data, psychological profiles can not only be constructed, but they can also be sought and found. For example if you’re looking for worried fathers, or angry introverts, or undecided Democrats. What Kosinski invented, to put it precisely, is a search engine for people. And he has been getting more and more acutely aware of both the potential and the danger his work presents. The internet always seemed to him a gift from heaven. He wants to give back, to share. Information is freely reproducible, copyable, and everyone should benefit from it. This is the spirit of an entire generation, the beginning of a new era free of the limits of the physical world. But what could happen, Kosinski asked himself, if someone misused his search engine in order to manipulate people? His scientific work [e.g.] began to come with warnings: these prediction techniques could be used in ways that “pose a threat to an individual’s well-being, freedom, or even life.” But no one seemed to understand what he meant.

Around this time, in early 2014, a young assistant professor named Aleksandr Kogan approached Kosinski. He said he had received an inquiry from a company interested in Kosinski’s methods. They apparently wanted to psychometrically measure the profiles of ten million American Facebook users. To what purpose, Kogan couldn’t say: there were strict secrecy stipulations. At first, Kosinski was ready to accept—it would have meant a lot of money for his institute. But he hesitated. Finally Kogan divulged the name of the company: SCL, Strategic Communications Laboratories. Kosinski googled them [so did Antidote. Here.]: “We are a global election management agency,” said the company website [really, the website has even creepier language on it than that. “Behavioral change communication”? Go look already]. SCL offers marketing based on a “psychographic targeting” model. With an emphasis on “election management” and political campaigns? Disturbed, Kosinski clicked through the pages. What kind of company is this? And what do they have planned for the United States?

What Kosinski didn’t know at the time was that behind SCL there lay a complex business structure including ancillary companies in tax havens, as the Panama Papers and Wikileaks revelations have since shown. Some of these had been involved in political upheavals in developing countries; others had done work for NATO, developing methods for the psychological manipulation of the population in Afghanistan. And SCL is also the parent company of Cambridge Analytica, this ominous Big Data firm that managed online marketing for both Trump and the Brexit “Leave” campaign.

Kosinski didn’t know any of that, but he had a bad feeling: “The whole thing started to stink,” he remembers. Looking into it further, he discovered that Aleksandr Kogan had secretly registered a company to do business with SCL. A document obtained by Das Magazin confirms that SCL learned about Kosinski’s methods through Kogan. It suddenly dawned on Kosinski that Kogan could have copied or reconstructed his Ocean models in order to sell them to this election-manipulating company. He immediately broke off contact with him and informed the head of his institute. A complicated battle ensued within Cambridge University. The institute feared for its reputation. Aleksandr Kogan moved to Singapore, got married, and began calling himself Dr. Spectre. Michal Kosinski relocated to Stanford University in the United States.

For a year or so it was quiet. Then, in November 2015, the more radical of the two Brexit campaigns (leave.eu, led by Nigel Farage) announced that they had contracted with a Big Data firm for online marketing support: Cambridge Analytica. The core expertise of this company: innovative political marketing, so-called microtargeting, on the basis of the psychological Ocean model. Kosinski started getting emails asking if he had had anything to do with it—for many, his is the first name to spring to mind upon hearing the terms Cambridge, Ocean, and analytics in the same breath. This is when he heard of Cambridge Analytica for the first time. Appalled, he looked up their website. His methods were being deployed, on a massive scale, for political purposes. After the Brexit vote in July the email inquiries turned to insults and reproaches. Just look what you’ve done, friends and colleagues wrote. Kosinski had to explain over and over again that he had nothing to do with this company.

First Brexit, Then Trump
September 19th, 2016: the US presidential election is approaching. Guitar riffs fill the dark blue ballroom of the Grand Hyatt Hotel in New York: CCR’s “Bad Moon Rising.” The Concordia Summit is like the WEF in miniature. Decision makers from all over the world are invited; among the guests is Johann Schneider-Ammann [then nearing the end of his year term as president of Switzerland’s governing council]. A gentle women’s voice comes over the PA: “Please welcome Alexander Nix, Chief Executive Officer of Cambridge Analytica.” A lean man in a dark suit strides towards the center of the stage. An attentive quiet descends. Many in the room already know: this is Trump’s new Digital Man. “Soon you’ll be calling me Mr. Brexit,” Trump had tweeted cryptically a few weeks before. Political observers had already been pointing out the substantial similarities between Trump’s agenda and that of the rightwing Brexit camp; only a few had noticed the connection to Trump’s recent engagement with a largely unknown marketing company: Cambridge Analytica.


Brad Parscale

Before then, Trump’s online campaign had consisted more or less of one person: Brad Parscale, a marketing operative and failed startup founder who had built Trump a rudimentary website for $1,500. The 70-year-old Trump is not what one would call an IT-whiz; his desk is unencumbered by a computer. There is no such thing as an email from Trump, his personal assistant once let slip. It was she who persuaded him to get a smartphone—the one from which he has uninhibitedly tweeted ever since. Hillary Clinton, on the other hand, was relying on the endowment of the first social media president, Barack Obama. She had the Democratic Party’s address lists, collected millions of dollars over the internet, received support from Google and Dreamworks. When it became known in June 2016 that Trump had hired Cambridge Analytica, Washington collectively sneered. Foreign noodlenecks in tailored suits who don’t understand this country and its people? Seriously?


“Cambridge Analytica uses the two columns on the left to deduce the right column, for each individual”

“Ladies and gentlemen, honorable colleagues, it is my privilege to speak to you today about the power of Big Data and psychographics in the electoral process.” The Cambridge Analytica logo appears behind Alexander Nix—a brain, comprised of a few network nodes and pathways, like a subway map. “It’s easy to forget that only eighteen months ago Senator Cruz was one of the less popular candidates seeking nomination, and certainly one of the more vilified,” begins the blond man with his British diction that produces the same mixture of awe and resentment in Americans that high German does the Swiss. “In addition, he had very low name recognition; only about forty percent of the electorate had heard of him.”

Everyone in the room was aware of the sudden rise, in May 2016, of the conservative senator within the Republican field of presidential candidates. It was one of the strangest moments of the primary campaign. Cruz had been the last of a series of Republican opponents to come out of nowhere with what looked like a credible challenge to frontrunner Trump. “How did he do this?” continues Nix. Cambridge Analytica had begun engaging with US elections towards the end of 2014, initially to advise the Republican Ted Cruz, and paid by the secretive American tech billionaire Robert Mercer. Up to that point, according to Nix, election campaign strategy had been guided by demographic concepts. “But this is a really ridiculous idea, the idea that all women should receive the same message because of their gender; or all African-Americans because of their race.”

The Hillary Clinton campaign team was still operating on precisely such amateurish assumptions—Nix need not even mention—which divide the electorate up into ostensibly homogeneous groups… exactly the same way as all the public opinion researchers who predicted a Clinton victory did. Nix clicks to the next slide: five different faces, each representing a personality profile. It is the Ocean model. “At Cambridge, we’ve rolled out a long-form quantitative instrument to probe the underlying traits that inform personality. This is the cutting edge in experimental psychology.” It is now completely silent in the hall. “By having hundreds and hundreds of thousands of Americans undertake this survey, we were able to form a model to predict the personality of every single adult in the United States of America.” The success of Cambridge Analytica’s marketing arises from the combination of three elements: this psychological behavioral analysis of the Ocean model, Big Data evaluation, and ad targeting. Ad targeting is personalized advertisement tailored as precisely as possible to the character of a single consumer.

Nix explains forthrightly how his company does this (the presentation can be viewed on YouTube). From every available source, Cambridge Analytica buys up personal data: “What car you drive, what products you purchase in shops, what magazines you read, what clubs you belong to.” Voter and medical records. On the screen behind him are displayed the logos of global data traders like Acxiom and Experian—in the United States nearly all personal consumer data is available for purchase. If you want to know, for example, where Jewish women live, you can simply buy this information. Including telephone numbers. Now Cambridge Analytica crosschecks these data sets with Republican Party voter rolls and online data such as Facebook likes, and constructs an Ocean personality profile. From a selection of digital signatures there suddenly emerge real individual people with fears, needs, and interests—and home addresses. The process is identical to the models that Michal Kosinski developed. Cambridge Analytica also uses IQ-Quiz and other small Ocean test apps in order to gain access to the powerful predictive personal information wrapped up in the Facebook likes of users.

And Cambridge Analytica is doing precisely what Kosinski had warned about. They have assembled psychograms for all adult US citizens, 220 million people, and have used this data to influence electoral outcomes. Nix clicks to the next slide. “This is a data dashboard that we prepared for the Cruz campaign for the Iowa caucus. It looks intimidating, but it’s actually very simple.” On the left, graphs and diagrams; on the right, a map of Iowa, where Cruz had done surprisingly well in the caucuses. On this map, hundreds of thousands of tiny dots, red and blue. Nix begins to narrow down search criteria to a category of Republican caucus-goers he describes as a “persuasion” group, whose common Ocean personality profile and home locations are now visible, a smaller set of people to whom advertisement can be more effectively tailored. Ultimately the criteria can be narrowed to a single individual, along with his name, age, address, interests, and political leanings. How does Cambridge Analyica approach this person with political messaging?

Earlier in the presentation, using the example of the Second Amendment, Nix showed two variations on how certain psychographic profiles are spoken to differently. “For a highly Neurotic and Conscientious audience, you’re going to need a message that is both rational and fear-based: the threat of a burglary and the ‘insurance policy’ of a gun is very persuasive.” A picture on the left side of the screen shows a gloved hand breaking a window and reaching for the inside door handle. On the right side, there is a picture of a man and child silhouetted against a sunset in tall grass, both with rifles, obviously duck hunting: “for a Closed and Agreeable audience, people who care about traditions and habits and family and community, talking about these values is going to be much more effective in communicating your message.”

How to Keep Clinton Voters Away
Trump’s conspicuous contradictions and his oft-criticized habit of staking out multiple positions on a single issue result in a gigantic number of resulting messaging options that creates a huge advantage for a firm like Cambridge Analytica: for every voter, a different message. Mathematician Cathy O’Neil had already observed in August that “Trump is like a machine learning algorithm” that adjusts to public reactions. On the day of the third presidential debate between Trump and Clinton, Trump’s team blasted out 175,000 distinct variations on his arguments, mostly via Facebook. The messages varied mostly in their microscopic details, in order to communicate optimally with their recipients: different titles, colors, subtitles, with different images or videos. The granularity of this message tailoring digs all the way down to tiny target groups, Nix explained to Das Magazin. “We can target specific towns or apartment buildings. Even individual people.”

In the Miami neighborhood of Little Haiti, Cambridge Analytica regaled residents with messages about the failures of the Clinton Foundation after the 2010 earthquake in Haiti, in order to dissuade them from turning out for Clinton. This was one of the goals: to get potential but wavering Clinton voters—skeptical leftists, African-Americans, young women—to stay home. To “suppress” their votes, as one Trump campaign staffer bluntly put it. In these so-called “dark posts” (paid Facebook ads which appear in the timelines only of users with a particular suitable personality profile), African-Americans, for example, are shown the nineties-era video of Hillary Clinton referring to black youth as “super predators”. “Blanket advertising—the idea that a hundred million people will receive the same piece of direct mail, the same television advert, the same digital advert—is dead,” Nix begins to wrap up his presentation at the Concordia Summit. “My children will certainly never understand this concept of mass communication. Today, communication is becoming ever increasingly targeted. “The Cruz campaign is over now, but what I can tell you is that of the two candidates left in this election, one of them is using these technologies. And it’s going to be very interesting to see how they impact the next seven weeks. Thank you.” With that, he exits the stage.

It is not knowable just to what extent the American population is being targeted by Trump’s digital troopers—because they seldom attack through the mainstream broadcast media, but rather mostly with highly personalized ads on social media or through digital cable. And while the Clinton team sat back in the confidence that it was safe with its demographic calculations, a new crew was moving into the Trump online campaign headquarters in San Antonio, Texas, as Bloomberg journalist Sasha Issenberg noted with surprise after a visit. The Cambridge Analytica team, apparently just a dozen people, had received around $100,000 from Trump in July; in August another $250,000; five million in September. Altogether, says Nix, they took in around fifteen million.

And the company took even more radical measures: starting in July 2016, a new app was prepared for Trump campaign canvassers with which they could find out the political orientation and personality profile of a particular house’s residents in advance. If the Trump people ring a doorbell, it’s only the doorbell of someone the app has identified as receptive to his messages, and the canvassers can base their line of attack on personality-specific conversation guides also provided by the app. Then they enter a subject’s reactions to certain messaging back into the app, from where this new data flows back to the control rooms of Cambridge Analytica. The company divided the US population into 32 personality types, and concentrated on only seventeen states. And just as Kosinski had determined that men who like MAC cosmetics on Facebook are probably gay, Cambridge Analytica found that a predeliction for American-produced cars is the best predictor of a possible Trump voter. Among other things, this kind of knowledge can inform Trump himself which messages to use, and where. The decision to focus candidate visits in Michigan and Wisconsin over the final weeks of the campaign was based on this manner of data analysis. The candidate himself became an implementation instrument of the model.

What is Cambridge Analytica Doing in Europe?
How great an influence did these psychometric methods have on the outcome of the election? Cambridge Analytica, when asked, did not want to disclose any documentation assessing the effectiveness of their campaign. It is possible that the question cannot be answered at all. Still, some indicators should be considered: there is the fact that Ted Cruz, thanks to the help of Cambridge Analytica, rose out of obscurity to become Trump’s strongest competitor in the primaries; there is the increase in rural voter turnout; there is the reduction, compared to 2008 and 2012, in African-American voter participation. The circumstance of Trump having spent so little money on advertising could also speak for the effectiveness of personality-specific targeting, as could the fact that three quarters of his marketing budget was spent in the digital realm. Facebook became his ultimate weapon and his best canvasser, as a Trump staffer tweeted. In Germany, the rightwing upstart party Alternative für Deutschland (AfD) may like the sound of this, as they have more Facebook friends than Merkel’s Christian Democrats (CDU) and the Social Democrats (SPD) combined.


“Voters who tend not to think much tend to prefer Trump” Source: Fording and Schram

It is therefore not at all the case, as is so often claimed, that statisticians lost this election because their polls were so faulty. The opposite is true: statisticians won this election. It was just certain statisticians, the ones using the new method. It is a cruel irony of history that Trump, such a detractor of science, won the election thanks to science. Another big winner in the election was Cambridge Analytica. Steve Bannon, a Cambridge Analytica board member and publisher of the ultra-rightwing online site Breitbart News, was named Trump’s chief strategist. Marion Maréchal-Le Pen, ambitious Front National activist and niece of the presidential candidate, has tweeted that she has accepted the firm’s invitation to collaborate. In an internal company video, there is a live recording of a discussion entitled “Italy.” Alexander Nix confirms that he is in the process of client acquisition, worldwide. They have received inquiries out of Switzerland and Germany.

Kosinski has been observing all of this from his office at Stanford. After the election, the university was in an uproar. Kosinski responded to the developments with the most powerful weapon available to researchers: a scientific analysis. Along with his research colleague Sandra Matz, he conducted a series of tests that will soon be published. The first results seen by Das Magazin are unsettling: psychological targeting, as Cambridge Analytica deployed it, increases the clickthru rate on Facebook ads by more than sixty percent. And the so-called conversion rate (the term for how likely a person is to act upon a personally-tailored ad, i.e. whether they buy a product or, yes, go vote) increases by a staggering 1400 percent. “The world has been turned upside down. The Brits are leaving the EU; Trump rules America. It all began with one man, who indeed tried to warn of the danger, and who still gets accusatory emails. “No,” says Kosinski quietly, shaking his head, “this is not my fault. I did not build the bomb. I just showed that it was there.”

* The study mentioned made a series of comparisons: a product was advertised online in two ways—one tailored to fit the character profile of a consumer and the other designed to clash with their character—and the respective conversion rates measured.

[Paul-Olivier Dehaye contributed to the preparation of the original article, which also included a link to his website where you can request your data from Cambridge Analytica: PersonalData.IO]

TURNKEY PSYOPS
https://tcf.org/content/commentary/turnkey-tyranny-jamming-lock-way/
https://medium.com/@pdehaye/microtargeting-of-low-information-voters-6eb2520cd473
https://medium.com/@pdehaye/the-dis-information-mercenaries-now-controlling-trumps-databases-4f6a20d4f3e7

The (dis)information mercenaries now controlling Trump’s databases
by Paul-Olivier Dehaye  /  1.2.2017

“Now that Trump, his SuperPACs and even their vendors have got a ton of data on every American, what could they do with that? We look at the PSYOPS dashboards that have been built by the same vendors to manipulate populations in Libya, Afghanistan and countless other countries. It has been widely reported that the Trump campaign used a company called Cambridge Analytica to help with voter targeting. That company boasts of having thousands of data points on every American, which helps with their micro-targeting of messages based on psychological traits. Thanks to millions of donations, Trump has built up a separate database of supporters, and their interactions with the campaign (rally attendance, donations, retweets, surveys, focus groups, etc). However, the actual targeting of a message is only just the endgame if you want to influence or manipulate a whole population. Indeed, Cambridge Analytica’s parent company, SCL Group, has taken its name from the whole field of study: Strategic Communication Laboratories. This group has a variety of affiliates, such as SCL Defence or SCL Elections, that have offered services in countless countries [1]. It also has its own think thank, the Behavioural Dynamics Institute, ultimately responsible for formalising the methodology, and even a(n Information Operations) Training & Advisory services branch, IOTA-Global…”

“…Technically, in Western democracies, a huge amount of feedback could be made to flow back to the dashboard (social media metrics, for instance). It could be that every single indicator in those dashboards is the result of a computation spanning millions of tweets, for instance. I now turn speculative, but what is described here makes me worried about a turnkey-PSYOPS [5]: if you bring along enough data, and integrate enough tools, one could construct similar dashboards that on a daily basis would monitor, move or keep the different audiences exactly where you would want them, in terms of fears, behaviours and emotions. If you think about it, you don’t even really need all the dashboards to be integrated in one place. A fragmented media ecosystem, each talking to a specific audience and optimizing for its own metrics is enough, as long as you have a way to integrate identities across audiences, and reach back to individuals (social media buttons do exactly that). Hence, one very effective way to do all this, regardless of ultimate intent, would be to bypass “media professionals” by creating alternative media outlets for each of the targeted audiences. The cherry on the cake would be to treat the mainstream media as a separate audience, itself subject to your manipulation. Indeed, by playing on their own anxieties (for instance with social media metrics or fear of partiality), the discussion could easily be steered exactly where and when you would want.

Of course, the above paragraph could refer to either Putin and/or Trump (via Mercer, who funded Breitbart). Both could have done or could do this, and use all kinds of data or information (generated themselves, even if nonsensical, false, stolen, etc). What kind of responses should we expect from other countries to such actions? Well, it turns out that SCL has a solution on hand! Indeed, IOTA-Global, the training arm of SCL, has delivered an 8 weeks course at the NATO Strategic Communications Centre of Excellence in Riga (Latvia), funded by Canada(!) and whose goal is to train NATO Eastern armies on countering Russian and ISIS propaganda with exactly those same tools. They have also research contracts with the Norwegian Army, and training contracts in Georgia, Moldova and Ukraine…”

UPDATE: “Trump’s digital campaign director, Brad Parscale, was just announced as a speaker at the Summit on Strategic Communications, held in Arlington Virginia, and sponsored by Bell Helicopter, a shipbuilding company, and Northrop Grumman.”

PREVIOUSLY


“Government hackers at the British intelligence agency have services to offer beyond those of their colleagues at the NSA, including the ability to hack into Gmail.”

HER MAJESTY’s WATCHERS
https://spectrevision.net/2016/12/07/her-majestys-watchers/