Entries Tagged "secrecy"

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Attributing the DNC Hacks to Russia

President Barack Obama’s public accusation of Russia as the source of the hacks in the US presidential election and the leaking of sensitive e-mails through WikiLeaks and other sources has opened up a debate on what constitutes sufficient evidence to attribute an attack in cyberspace. The answer is both complicated and inherently tied up in political considerations.

The administration is balancing political considerations and the inherent secrecy of electronic espionage with the need to justify its actions to the public. These issues will continue to plague us as more international conflict plays out in cyberspace.

It’s true that it’s easy for an attacker to hide who he is in cyberspace. We are unable to identify particular pieces of hardware and software around the world positively. We can’t verify the identity of someone sitting in front of a keyboard through computer data alone. Internet data packets don’t come with return addresses, and it’s easy for attackers to disguise their origins. For decades, hackers have used techniques such as jump hosts, VPNs, Tor and open relays to obscure their origin, and in many cases they work. I’m sure that many national intelligence agencies route their attacks through China, simply because everyone knows lots of attacks come from China.

On the other hand, there are techniques that can identify attackers with varying degrees of precision. It’s rarely just one thing, and you’ll often hear the term “constellation of evidence” to describe how a particular attacker is identified. It’s analogous to traditional detective work. Investigators collect clues and piece them together with known mode of operations. They look for elements that resemble other attacks and elements that are anomalies. The clues might involve ones and zeros, but the techniques go back to Sir Arthur Conan Doyle.

The University of Toronto-based organization Citizen Lab routinely attributes attacks against the computers of activists and dissidents to particular Third World governments. It took months to identify China as the source of the 2012 attacks against the New York Times. While it was uncontroversial to say that Russia was the source of a cyberattack against Estonia in 2007, no one knew if those attacks were authorized by the Russian government—until the attackers explained themselves. And it was the Internet security company CrowdStrike, which first attributed the attacks against the Democratic National Committee to Russian intelligence agencies in June, based on multiple pieces of evidence gathered from its forensic investigation.

Attribution is easier if you are monitoring broad swaths of the Internet. This gives the National Security Agency a singular advantage in the attribution game. The problem, of course, is that the NSA doesn’t want to publish what it knows.

Regardless of what the government knows and how it knows it, the decision of whether to make attribution evidence public is another matter. When Sony was attacked, many security experts—myself included­—were skeptical of both the government’s attribution claims and the flimsy evidence associated with it. I only became convinced when the New York Times ran a story about the government’s attribution, which talked about both secret evidence inside the NSA and human intelligence assets inside North Korea. In contrast, when the Office of Personnel Management was breached in 2015, the US government decided not to accuse China publicly, either because it didn’t want to escalate the political situation or because it didn’t want to reveal any secret evidence.

The Obama administration has been more public about its evidence in the DNC case, but it has not been entirely public.

It’s one thing for the government to know who attacked it. It’s quite another for it to convince the public who attacked it. As attribution increasingly relies on secret evidence­—as it did with North Korea’s attack of Sony in 2014 and almost certainly does regarding Russia and the previous election—­the government is going to have to face the choice of making previously secret evidence public and burning sources and methods, or keeping it secret and facing perfectly reasonable skepticism.

If the government is going to take public action against a cyberattack, it needs to make its evidence public. But releasing secret evidence might get people killed, and it would make any future confidentiality assurances we make to human sources completely non-credible. This problem isn’t going away; secrecy helps the intelligence community, but it wounds our democracy.

The constellation of evidence attributing the attacks against the DNC, and subsequent release of information, is comprehensive. It’s possible that there was more than one attack. It’s possible that someone not associated with Russia leaked the information to WikiLeaks, although we have no idea where that someone else would have obtained the information. We know that the Russian actors who hacked the DNC­—both the FSB, Russia’s principal security agency, and the GRU, Russia’s military intelligence unit—­are also attacking other political networks around the world.

In the end, though, attribution comes down to whom you believe. When Citizen Lab writes a report outlining how a United Arab Emirates human rights defender was targeted with a cyberattack, we have no trouble believing that it was the UAE government. When Google identifies China as the source of attacks against Gmail users, we believe it just as easily.

Obama decided not to make the accusation public before the election so as not to be seen as influencing the election. Now, afterward, there are political implications in accepting that Russia hacked the DNC in an attempt to influence the US presidential election. But no amount of evidence can convince the unconvinceable.

The most important thing we can do right now is deter any country from trying this sort of thing in the future, and the political nature of the issue makes that harder. Right now, we’ve told the world that others can get away with manipulating our election process as long as they can keep their efforts secret until after one side wins. Obama has promised both secret retaliations and public ones. We need to hope they’re enough.

This essay previously appeared on CNN.com.

EDITED TO ADD: The ODNI released a declassified report on the Russian attacks. Here’s a New York Times article on the report.

And last week there were Senate hearings on this issue.

EDITED TO ADD: A Washington Post article talks about some of the intelligence behind the assessment.

EDITED TO ADD (1/10): The UK connection.

Posted on January 9, 2017 at 5:53 AM • View Comments

Automatically Identifying Government Secrets

Interesting research: “Using Artificial Intelligence to Identify State Secrets,” by Renato Rocha Souza, Flavio Codeco Coelho, Rohan Shah, and Matthew Connelly.

Abstract: Whether officials can be trusted to protect national security information has become a matter of great public controversy, reigniting a long-standing debate about the scope and nature of official secrecy. The declassification of millions of electronic records has made it possible to analyze these issues with greater rigor and precision. Using machine-learning methods, we examined nearly a million State Department cables from the 1970s to identify features of records that are more likely to be classified, such as international negotiations, military operations, and high-level communications. Even with incomplete data, algorithms can use such features to identify 90% of classified cables with <11% false positives. But our results also show that there are longstanding problems in the identification of sensitive information. Error analysis reveals many examples of both overclassification and underclassification. This indicates both the need for research on inter-coder reliability among officials as to what constitutes classified material and the opportunity to develop recommender systems to better manage both classification and declassification.

Posted on November 11, 2016 at 1:18 PM • View Comments

Intelligence Oversight and How It Can Fail

Former NSA attorneys John DeLong and Susan Hennessay have written a fascinating article describing a particular incident of oversight failure inside the NSA. Technically, the story hinges on a definitional difference between the NSA and the FISA court meaning of the word “archived.” (For the record, I would have defaulted to the NSA’s interpretation, which feels more accurate technically.) But while the story is worth reading, what’s especially interesting are the broader issues about how a nontechnical judiciary can provide oversight over a very technical data collection-and-analysis organization—especially if the oversight must largely be conducted in secret.

From the article:

Broader root cause analysis aside, the BR FISA debacle made clear that the specific matter of shared legal interpretation needed to be addressed. Moving forward, the government agreed that NSA would coordinate all significant legal interpretations with DOJ. That sounds like an easy solution, but making it meaningful in practice is highly complex. Consider this example: a court order might require that “all collected data must be deleted after two years.” NSA engineers must then make a list for the NSA attorneys:

  1. What does deleted mean? Does it mean make inaccessible to analysts or does it mean forensically wipe off the system so data is gone forever? Or does it mean something in between?
  2. What about backup systems used solely for disaster recovery? Does the data need to be removed there, too, within two years, even though it’s largely inaccessible and typically there is a planned delay to account for mistakes in the operational system?
  3. When does the timer start?
  4. What’s the legally-relevant unit of measurement for timestamp computation­—a day, an hour, a second, a millisecond?
  5. If a piece of data is deleted one second after two years, is that an incident of noncompliance? What about a delay of one day? ….
  6. What about various system logs that simply record the fact that NSA had a data object, but no significant details of the actual object? Do those logs need to be deleted too? If so, how soon?
  7. What about hard copy printouts?

And that is only a tiny sample of the questions that need to be answered for that small sentence fragment. Put yourself in the shoes of an NSA attorney: which of these questions—­in particular the answers­—require significant interpretations to be coordinated with DOJ and which determinations can be made internally?

Now put yourself in the shoes of a DOJ attorney who receives from an NSA attorney a subset of this list for advice and counsel. Which questions are truly significant from your perspective? Are there any questions here that are so significant they should be presented to the Court so that that government can be sufficiently confident that the Court understands how the two-year rule is really being interpreted and applied?

In many places I have separated different kinds of oversight: are we doing things right versus are we doing the right things? This is very much about the first: is the NSA complying with the rules the courts impose on them? I believe that the NSA tries very hard to follow the rules it’s given, while at the same time being very aggressive about how it interprets any kind of ambiguities and using its nonadversarial relationship with its overseers to its advantage.

The only possible solution I can see to all of this is more public scrutiny. Secrecy is toxic here.

Posted on October 18, 2016 at 2:29 PM • View Comments

The Mathematics of Conspiracy

This interesting study tries to build a mathematical model for the continued secrecy of conspiracies, and tries to predict how long before they will be revealed to the general public, either wittingly or unwittingly.

The equation developed by Dr Grimes, a post-doctoral physicist at Oxford, relied upon three factors: the number of conspirators involved, the amount of time that has passed, and the intrinsic probability of a conspiracy failing.

He then applied his equation to four famous conspiracy theories: The belief that the Moon landing was faked, the belief that climate change is a fraud, the belief that vaccines cause autism, and the belief that pharmaceutical companies have suppressed a cure for cancer.

Dr Grimes’s analysis suggests that if these four conspiracies were real, most are very likely to have been revealed as such by now.

Specifically, the Moon landing “hoax” would have been revealed in 3.7 years, the climate change “fraud” in 3.7 to 26.8 years, the vaccine-autism “conspiracy” in 3.2 to 34.8 years, and the cancer “conspiracy” in 3.2 years.

He also ran the model against two actual conspiracies: the NSA’s PRISM program and the Tuskegee syphilis experiment.

From the paper:

Abstract: Conspiratorial ideation is the tendency of individuals to believe that events and power relations are secretly manipulated by certain clandestine groups and organisations. Many of these ostensibly explanatory conjectures are non-falsifiable, lacking in evidence or demonstrably false, yet public acceptance remains high. Efforts to convince the general public of the validity of medical and scientific findings can be hampered by such narratives, which can create the impression of doubt or disagreement in areas where the science is well established. Conversely, historical examples of exposed conspiracies do exist and it may be difficult for people to differentiate between reasonable and dubious assertions. In this work, we establish a simple mathematical model for conspiracies involving multiple actors with time, which yields failure probability for any given conspiracy. Parameters for the model are estimated from literature examples of known scandals, and the factors influencing conspiracy success and failure are explored. The model is also used to estimate the likelihood of claims from some commonly-held conspiratorial beliefs; these are namely that the moon-landings were faked, climate-change is a hoax, vaccination is dangerous and that a cure for cancer is being suppressed by vested interests. Simulations of these claims predict that intrinsic failure would be imminent even with the most generous estimates for the secret-keeping ability of active participants­—the results of this model suggest that large conspiracies (≥1000 agents) quickly become untenable and prone to failure. The theory presented here might be useful in counteracting the potentially deleterious consequences of bogus and anti-science narratives, and examining the hypothetical conditions under which sustainable conspiracy might be possible.

Lots on the psychology of conspiracy theories here.

EDITED TO ADD (3/12): This essay debunks the above research.

Posted on March 2, 2016 at 12:39 PM • View Comments

UK Government Promoting Backdoor-Enabled Voice Encryption Protocol

The UK government is pushing something called the MIKEY-SAKKE protocol to secure voice. Basically, it’s an identity-based system that necessarily requires a trusted key-distribution center. So key escrow is inherently built in, and there’s no perfect forward secrecy. The only reasonable explanation for designing a protocol with these properties is third-party eavesdropping.

Steven Murdoch has explained the details. The upshot:

The design of MIKEY-SAKKE is motivated by the desire to allow undetectable and unauditable mass surveillance, which may be a requirement in exceptional scenarios such as within government departments processing classified information. However, in the vast majority of cases the properties that MIKEY-SAKKE offers are actively harmful for security. It creates a vulnerable single point of failure, which would require huge effort, skill and cost to secure ­ requiring resource beyond the capability of most companies. Better options for voice encryption exist today, though they are not perfect either. In particular, more work is needed on providing scalable and usable protection against man-in-the-middle attacks, and protection of metadata for contact discovery and calls. More broadly, designers of protocols and systems need to appreciate the ethical consequences of their actions in terms of the political and power structures which naturally follow from their use. MIKEY-SAKKE is the latest example to raise questions over the policy of many governments, including the UK, to put intelligence agencies in charge of protecting companies and individuals from spying, given the conflict of interest it creates.

And GCHQ previously rejected a more secure standard, MIKEY-IBAKE, because it didn’t allow undetectable spying.

Both the NSA and GCHQ repeatedly choose surveillance over security. We need to reject that decision.

Posted on January 22, 2016 at 2:23 PM • View Comments

Replacing Judgment with Algorithms

China is considering a new “social credit” system, designed to rate everyone’s trustworthiness. Many fear that it will become a tool of social control—but in reality it has a lot in common with the algorithms and systems that score and classify us all every day.

Human judgment is being replaced by automatic algorithms, and that brings with it both enormous benefits and risks. The technology is enabling a new form of social control, sometimes deliberately and sometimes as a side effect. And as the Internet of Things ushers in an era of more sensors and more data—and more algorithms—we need to ensure that we reap the benefits while avoiding the harms.

Right now, the Chinese government is watching how companies use “social credit” scores in state-approved pilot projects. The most prominent one is Sesame Credit, and it’s much more than a financial scoring system.

Citizens are judged not only by conventional financial criteria, but by their actions and associations. Rumors abound about how this system works. Various news sites are speculating that your score will go up if you share a link from a state-sponsored news agency and go down if you post pictures of Tiananmen Square. Similarly, your score will go up if you purchase local agricultural products and down if you purchase Japanese anime. Right now the worst fears seem overblown, but could certainly come to pass in the future.

This story has spread because it’s just the sort of behavior you’d expect from the authoritarian government in China. But there’s little about the scoring systems used by Sesame Credit that’s unique to China. All of us are being categorized and judged by similar algorithms, both by companies and by governments. While the aim of these systems might not be social control, it’s often the byproduct. And if we’re not careful, the creepy results we imagine for the Chinese will be our lot as well.

Sesame Credit is largely based on a US system called FICO. That’s the system that determines your credit score. You actually have a few dozen different ones, and they determine whether you can get a mortgage, car loan or credit card, and what sorts of interest rates you’re offered. The exact algorithm is secret, but we know in general what goes into a FICO score: how much debt you have, how good you’ve been at repaying your debt, how long your credit history is and so on.

There’s nothing about your social network, but that might change. In August, Facebook was awarded a patent on using a borrower’s social network to help determine if he or she is a good credit risk. Basically, your creditworthiness becomes dependent on the creditworthiness of your friends. Associate with deadbeats, and you’re more likely to be judged as one.

Your associations can be used to judge you in other ways as well. It’s now common for employers to use social media sites to screen job applicants. This manual process is increasingly being outsourced and automated; companies like Social Intelligence, Evolv and First Advantage automatically process your social networking activity and provide hiring recommendations for employers. The dangers of this type of system—from discriminatory biases resulting from the data to an obsession with scores over more social measures—are too many.

The company Klout tried to make a business of measuring your online influence, hoping its proprietary system would become an industry standard used for things like hiring and giving out free product samples.

The US government is judging you as well. Your social media postings could get you on the terrorist watch list, affecting your ability to fly on an airplane and even get a job. In 2012, a British tourist’s tweet caused the US to deny him entry into the country. We know that the National Security Agency uses complex computer algorithms to sift through the Internet data it collects on both Americans and foreigners.

All of these systems, from Sesame Credit to the NSA’s secret algorithms, are made possible by computers and data. A couple of generations ago, you would apply for a home mortgage at a bank that knew you, and a bank manager would make a determination of your creditworthiness. Yes, the system was prone to all sorts of abuses, ranging from discrimination to an old-boy network of friends helping friends. But the system also couldn’t scale. It made no sense for a bank across the state to give you a loan, because they didn’t know you. Loans stayed local.

FICO scores changed that. Now, a computer crunches your credit history and produces a number. And you can take that number to any mortgage lender in the country. They don’t need to know you; your score is all they need to decide whether you’re trustworthy.

This score enabled the home mortgage, car loan, credit card and other lending industries to explode, but it brought with it other problems. People who don’t conform to the financial norm—having and using credit cards, for example—can have trouble getting loans when they need them. The automatic nature of the system enforces conformity.

The secrecy of the algorithms further pushes people toward conformity. If you are worried that the US government will classify you as a potential terrorist, you’re less likely to friend Muslims on Facebook. If you know that your Sesame Credit score is partly based on your not buying “subversive” products or being friends with dissidents, you’re more likely to overcompensate by not buying anything but the most innocuous books or corresponding with the most boring people.

Uber is an example of how this works. Passengers rate drivers and drivers rate passengers; both risk getting booted out of the system if their rankings get too low. This weeds out bad drivers and passengers, but also results in marginal people being blocked from the system, and everyone else trying to not make any special requests, avoid controversial conversation topics, and generally behave like good corporate citizens.

Many have documented a chilling effect among American Muslims, with them avoiding certain discussion topics lest they be taken the wrong way. Even if nothing would happen because of it, their free speech has been curtailed because of the secrecy surrounding government surveillance. How many of you are reluctant to Google “pressure cooker bomb”? How many are a bit worried that I used it in this essay?

This is what social control looks like in the Internet age. The Cold-War-era methods of undercover agents, informants living in your neighborhood, and agents provocateur is too labor-intensive and inefficient. These automatic algorithms make possible a wholly new way to enforce conformity. And by accepting algorithmic classification into our lives, we’re paving the way for the same sort of thing China plans to put into place.

It doesn’t have to be this way. We can get the benefits of automatic algorithmic systems while avoiding the dangers. It’s not even hard.

The first step is to make these algorithms public. Companies and governments both balk at this, fearing that people will deliberately try to game them, but the alternative is much worse.

The second step is for these systems to be subject to oversight and accountability. It’s already illegal for these algorithms to have discriminatory outcomes, even if they’re not deliberately designed in. This concept needs to be expanded. We as a society need to understand what we expect out of the algorithms that automatically judge us and ensure that those expectations are met.

We also need to provide manual systems for people to challenge their classifications. Automatic algorithms are going to make mistakes, whether it’s by giving us bad credit scores or flagging us as terrorists. We need the ability to clear our names if this happens, through a process that restores human judgment.

Sesame Credit sounds like a dystopia because we can easily imagine how the Chinese government can use a system like this to enforce conformity and stifle dissent. Our own systems seem safer, because we don’t believe the corporations and governments that run them are malevolent. But the dangers are inherent in the technologies. As we move into a world where we are increasingly judged by algorithms, we need to ensure that they do so fairly and properly.

This essay previously appeared on CNN.com.

Posted on January 8, 2016 at 5:21 AM • View Comments

A History of Privacy

This New Yorker article traces the history of privacy from the mid 1800s to today:

As a matter of historical analysis, the relationship between secrecy and privacy can be stated in an axiom: the defense of privacy follows, and never precedes, the emergence of new technologies for the exposure of secrets. In other words, the case for privacy always comes too late. The horse is out of the barn. The post office has opened your mail. Your photograph is on Facebook. Google already knows that, notwithstanding your demographic, you hate kale.

Posted on November 30, 2015 at 12:47 PM • View Comments

"The Declining Half-Life of Secrets"

Several times I’ve mentioned Peter Swire’s concept of “the declining half-life of secrets.” He’s finally written it up:

The nature of secrets is changing. Secrets that would once have survived the 25 or 50 year test of time are more and more prone to leaks. The declining half-life of secrets has implications for the intelligence community and other secretive agencies, as they must now wrestle with new challenges posed by the transformative power of information technology innovation as well as the changing methods and targets of intelligence collection.

Posted on September 3, 2015 at 8:43 AM • View Comments

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Sidebar photo of Bruce Schneier by Joe MacInnis.