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Palantir’s Surveillance Service for Law Enforcement

Motherboard got its hands on Palantir’s Gotham user’s manual, which is used by the police to get information on people:

The Palantir user guide shows that police can start with almost no information about a person of interest and instantly know extremely intimate details about their lives. The capabilities are staggering, according to the guide:

  • If police have a name that’s associated with a license plate, they can use automatic license plate reader data to find out where they’ve been, and when they’ve been there. This can give a complete account of where someone has driven over any time period.

  • With a name, police can also find a person’s email address, phone numbers, current and previous addresses, bank accounts, social security number(s), business relationships, family relationships, and license information like height, weight, and eye color, as long as it’s in the agency’s database.

  • The software can map out a person’s family members and business associates of a suspect, and theoretically, find the above information about them, too.

All of this information is aggregated and synthesized in a way that gives law enforcement nearly omniscient knowledge over any suspect they decide to surveil.

Read the whole article — it has a lot of details. This seems like a commercial version of the NSA’s XKEYSCORE.

Boing Boing post.

Meanwhile:

The FBI wants to gather more information from social media. Today, it issued a call for contracts for a new social media monitoring tool. According to a request-for-proposals (RFP), it’s looking for an “early alerting tool” that would help it monitor terrorist groups, domestic threats, criminal activity and the like.

The tool would provide the FBI with access to the full social media profiles of persons-of-interest. That could include information like user IDs, emails, IP addresses and telephone numbers. The tool would also allow the FBI to track people based on location, enable persistent keyword monitoring and provide access to personal social media history. According to the RFP, “The mission-critical exploitation of social media will enable the Bureau to detect, disrupt, and investigate an ever growing diverse range of threats to U.S. National interests.”

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US Journalist Detained When Returning to US

Pretty horrible story of a US journalist who had his computer and phone searched at the border when returning to the US from Mexico.

After I gave him the password to my iPhone, Moncivias spent three hours reviewing hundreds of photos and videos and emails and calls and texts, including encrypted messages on WhatsApp, Signal, and Telegram. It was the digital equivalent of tossing someone’s house: opening cabinets, pulling out drawers, and overturning furniture in hopes of finding something — anything — illegal. He read my communications with friends, family, and loved ones. He went through my correspondence with colleagues, editors, and sources. He asked about the identities of people who have worked with me in war zones. He also went through my personal photos, which I resented. Consider everything on your phone right now. Nothing on mine was spared.

Pomeroy, meanwhile, searched my laptop. He browsed my emails and my internet history. He looked through financial spreadsheets and property records and business correspondence. He was able to see all the same photos and videos as Moncivias and then some, including photos I thought I had deleted.

The EFF has extensive information and advice about device searches at the US border, including a travel guide:

If you are a U.S. citizen, border agents cannot stop you from entering the country, even if you refuse to unlock your device, provide your device password, or disclose your social media information. However, agents may escalate the encounter if you refuse. For example, agents may seize your devices, ask you intrusive questions, search your bags more intensively, or increase by many hours the length of detention. If you are a lawful permanent resident, agents may raise complicated questions about your continued status as a resident. If you are a foreign visitor, agents may deny you entry.

The most important piece of advice is to think about this all beforehand, and plan accordingly.

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Digital License Plates

They’re a thing:

Developers say digital plates utilize “advanced telematics” — to collect tolls, pay for parking and send out Amber Alerts when a child is abducted. They also help recover stolen vehicles by changing the display to read “Stolen,” thereby alerting everyone within eyeshot.

This makes no sense to me. The numbers are static. License plates being low-tech are a feature, not a bug.

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Spanish Soccer League App Spies on Fans

The Spanish Soccer League’s smartphone app spies on fans in order to find bars that are illegally streaming its games. The app listens with the microphone for the broadcasts, and then uses geolocation to figure out where the phone is.

The Spanish data protection agency has ordered the league to stop doing this. Not because it’s creepy spying, but because the terms of service — which no one reads anyway — weren’t clear.

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iPhone Apps Surreptitiously Communicated with Unknown Servers

Long news article (alternate source) on iPhone privacy, specifically the enormous amount of data your apps are collecting without your knowledge. A lot of this happens in the middle of the night, when you’re probably not otherwise using your phone:

IPhone apps I discovered tracking me by passing information to third parties ­ just while I was asleep ­ include Microsoft OneDrive, Intuit’s Mint, Nike, Spotify, The Washington Post and IBM’s the Weather Channel. One app, the crime-alert service Citizen, shared personally identifiable information in violation of its published privacy policy.

And your iPhone doesn’t only feed data trackers while you sleep. In a single week, I encountered over 5,400 trackers, mostly in apps, not including the incessant Yelp traffic.

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How Apple’s “Find My” Feature Works

Matthew Green intelligently speculates about how Apple’s new “Find My” feature works.

If you haven’t already been inspired by the description above, let me phrase the question you ought to be asking: how is this system going to avoid being a massive privacy nightmare?

Let me count the concerns:

  • If your device is constantly emitting a BLE signal that uniquely identifies it, the whole world is going to have (yet another) way to track you. Marketers already use WiFi and Bluetooth MAC addresses to do this: Find My could create yet another tracking channel.

  • It also exposes the phones who are doing the tracking. These people are now going to be sending their current location to Apple (which they may or may not already be doing). Now they’ll also be potentially sharing this information with strangers who “lose” their devices. That could go badly.

  • Scammers might also run active attacks in which they fake the location of your device. While this seems unlikely, people will always surprise you.

The good news is that Apple claims that their system actually does provide strong privacy, and that it accomplishes this using clever cryptography. But as is typical, they’ve declined to give out the details how they’re going to do it. Andy Greenberg talked me through an incomplete technical description that Apple provided to Wired, so that provides many hints. Unfortunately, what Apple provided still leaves huge gaps. It’s into those gaps that I’m going to fill in my best guess for what Apple is actually doing.

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Maciej Cegłowski on Privacy in the Information Age

Maciej Cegłowski has a really good essay explaining how to think about privacy today:

For the purposes of this essay, I’ll call it “ambient privacy” — the understanding that there is value in having our everyday interactions with one another remain outside the reach of monitoring, and that the small details of our daily lives should pass by unremembered. What we do at home, work, church, school, or in our leisure time does not belong in a permanent record. Not every conversation needs to be a deposition.

Until recently, ambient privacy was a simple fact of life. Recording something for posterity required making special arrangements, and most of our shared experience of the past was filtered through the attenuating haze of human memory. Even police states like East Germany, where one in seven citizens was an informer, were not able to keep tabs on their entire population. Today computers have given us that power. Authoritarian states like China and Saudi Arabia are using this newfound capacity as a tool of social control. Here in the United States, we’re using it to show ads. But the infrastructure of total surveillance is everywhere the same, and everywhere being deployed at scale.

Ambient privacy is not a property of people, or of their data, but of the world around us. Just like you can’t drop out of the oil economy by refusing to drive a car, you can’t opt out of the surveillance economy by forswearing technology (and for many people, that choice is not an option). While there may be worthy reasons to take your life off the grid, the infrastructure will go up around you whether you use it or not.

Because our laws frame privacy as an individual right, we don’t have a mechanism for deciding whether we want to live in a surveillance society. Congress has remained silent on the matter, with both parties content to watch Silicon Valley make up its own rules. The large tech companies point to our willing use of their services as proof that people don’t really care about their privacy. But this is like arguing that inmates are happy to be in jail because they use the prison library. Confronted with the reality of a monitored world, people make the rational decision to make the best of it.

That is not consent.

Ambient privacy is particularly hard to protect where it extends into social and public spaces outside the reach of privacy law. If I’m subjected to facial recognition at the airport, or tagged on social media at a little league game, or my public library installs an always-on Alexa microphone, no one is violating my legal rights. But a portion of my life has been brought under the magnifying glass of software. Even if the data harvested from me is anonymized in strict conformity with the most fashionable data protection laws, I’ve lost something by the fact of being monitored.

He’s not the first person to talk about privacy as a societal property, or to use pollution metaphors. But his framing is really cogent. And “ambient privacy” is new — and a good phrasing.

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Data, Surveillance, and the AI Arms Race

According to foreign policy experts and the defense establishment, the United States is caught in an artificial intelligence arms race with China — one with serious implications for national security. The conventional version of this story suggests that the United States is at a disadvantage because of self-imposed restraints on the collection of data and the privacy of its citizens, while China, an unrestrained surveillance state, is at an advantage. In this vision, the data that China collects will be fed into its systems, leading to more powerful AI with capabilities we can only imagine today. Since Western countries can’t or won’t reap such a comprehensive harvest of data from their citizens, China will win the AI arms race and dominate the next century.

This idea makes for a compelling narrative, especially for those trying to justify surveillance — whether government- or corporate-run. But it ignores some fundamental realities about how AI works and how AI research is conducted.

Thanks to advances in machine learning, AI has flipped from theoretical to practical in recent years, and successes dominate public understanding of how it works. Machine learning systems can now diagnose pneumonia from X-rays, play the games of go and poker, and read human lips, all better than humans. They’re increasingly watching surveillance video. They are at the core of self-driving car technology and are playing roles in both intelligence-gathering and military operations. These systems monitor our networks to detect intrusions and look for spam and malware in our email.

And it’s true that there are differences in the way each country collects data. The United States pioneered “surveillance capitalism,” to use the Harvard University professor Shoshana Zuboff’s term, where data about the population is collected by hundreds of large and small companies for corporate advantage — and mutually shared or sold for profit The state picks up on that data, in cases such as the Centers for Disease Control and Prevention’s use of Google search data to map epidemics and evidence shared by alleged criminals on Facebook, but it isn’t the primary user.

China, on the other hand, is far more centralized. Internet companies collect the same sort of data, but it is shared with the government, combined with government-collected data, and used for social control. Every Chinese citizen has a national ID number that is demanded by most services and allows data to easily be tied together. In the western region of Xinjiang, ubiquitous surveillance is used to oppress the Uighur ethnic minority — although at this point there is still a lot of human labor making it all work. Everyone expects that this is a test bed for the entire country.

Data is increasingly becoming a part of control for the Chinese government. While many of these plans are aspirational at the moment — there isn’t, as some have claimed, a single “social credit score,” but instead future plans to link up a wide variety of systems — data collection is universally pushed as essential to the future of Chinese AI. One executive at search firm Baidu predicted that the country’s connected population will provide them with the raw data necessary to become the world’s preeminent tech power. China’s official goal is to become the world AI leader by 2030, aided in part by all of this massive data collection and correlation.

This all sounds impressive, but turning massive databases into AI capabilities doesn’t match technological reality. Current machine learning techniques aren’t all that sophisticated. All modern AI systems follow the same basic methods. Using lots of computing power, different machine learning models are tried, altered, and tried again. These systems use a large amount of data (the training set) and an evaluation function to distinguish between those models and variations that work well and those that work less well. After trying a lot of models and variations, the system picks the one that works best. This iterative improvement continues even after the system has been fielded and is in use.

So, for example, a deep learning system trying to do facial recognition will have multiple layers (hence the notion of “deep”) trying to do different parts of the facial recognition task. One layer will try to find features in the raw data of a picture that will help find a face, such as changes in color that will indicate an edge. The next layer might try to combine these lower layers into features like shapes, looking for round shapes inside of ovals that indicate eyes on a face. The different layers will try different features and will be compared by the evaluation function until the one that is able to give the best results is found, in a process that is only slightly more refined than trial and error.

Large data sets are essential to making this work, but that doesn’t mean that more data is automatically better or that the system with the most data is automatically the best system. Train a facial recognition algorithm on a set that contains only faces of white men, and the algorithm will have trouble with any other kind of face. Use an evaluation function that is based on historical decisions, and any past bias is learned by the algorithm. For example, mortgage loan algorithms trained on historic decisions of human loan officers have been found to implement redlining. Similarly, hiring algorithms trained on historical data manifest the same sexism as human staff often have. Scientists are constantly learning about how to train machine learning systems, and while throwing a large amount of data and computing power at the problem can work, more subtle techniques are often more successful. All data isn’t created equal, and for effective machine learning, data has to be both relevant and diverse in the right ways.

Future research advances in machine learning are focused on two areas. The first is in enhancing how these systems distinguish between variations of an algorithm. As different versions of an algorithm are run over the training data, there needs to be some way of deciding which version is “better.” These evaluation functions need to balance the recognition of an improvement with not over-fitting to the particular training data. Getting functions that can automatically and accurately distinguish between two algorithms based on minor differences in the outputs is an art form that no amount of data can improve.

The second is in the machine learning algorithms themselves. While much of machine learning depends on trying different variations of an algorithm on large amounts of data to see which is most successful, the initial formulation of the algorithm is still vitally important. The way the algorithms interact, the types of variations attempted, and the mechanisms used to test and redirect the algorithms are all areas of active research. (An overview of some of this work can be found here; even trying to limit the research to 20 papers oversimplifies the work being done in the field.) None of these problems can be solved by throwing more data at the problem.

The British AI company DeepMind’s success in teaching a computer to play the Chinese board game go is illustrative. Its AlphaGo computer program became a grandmaster in two steps. First, it was fed some enormous number of human-played games. Then, the game played itself an enormous number of times, improving its own play along the way. In 2016, AlphaGo beat the grandmaster Lee Sedol four games to one.

While the training data in this case, the human-played games, was valuable, even more important was the machine learning algorithm used and the function that evaluated the relative merits of different game positions. Just one year later, DeepMind was back with a follow-on system: AlphaZero. This go-playing computer dispensed entirely with the human-played games and just learned by playing against itself over and over again. It plays like an alien. (It also became a grandmaster in chess and shogi.)

These are abstract games, so it makes sense that a more abstract training process works well. But even something as visceral as facial recognition needs more than just a huge database of identified faces in order to work successfully. It needs the ability to separate a face from the background in a two-dimensional photo or video and to recognize the same face in spite of changes in angle, lighting, or shadows. Just adding more data may help, but not nearly as much as added research into what to do with the data once we have it.

Meanwhile, foreign-policy and defense experts are talking about AI as if it were the next nuclear arms race, with the country that figures it out best or first becoming the dominant superpower for the next century. But that didn’t happen with nuclear weapons, despite research only being conducted by governments and in secret. It certainly won’t happen with AI, no matter how much data different nations or companies scoop up.

It is true that China is investing a lot of money into artificial intelligence research: The Chinese government believes this will allow it to leapfrog other countries (and companies in those countries) and become a major force in this new and transformative area of computing — and it may be right. On the other hand, much of this seems to be a wasteful boondoggle. Slapping “AI” on pretty much anything is how to get funding. The Chinese Ministry of Education, for instance, promises to produce “50 world-class AI textbooks,” with no explanation of what that means.

In the democratic world, the government is neither the leading researcher nor the leading consumer of AI technologies. AI research is much more decentralized and academic, and it is conducted primarily in the public eye. Research teams keep their training data and models proprietary but freely publish their machine learning algorithms. If you wanted to work on machine learning right now, you could download Microsoft’s Cognitive Toolkit, Google’s Tensorflow, or Facebook’s Pytorch. These aren’t toy systems; these are the state-of-the art machine learning platforms.

AI is not analogous to the big science projects of the previous century that brought us the atom bomb and the moon landing. AI is a science that can be conducted by many different groups with a variety of different resources, making it closer to computer design than the space race or nuclear competition. It doesn’t take a massive government-funded lab for AI research, nor the secrecy of the Manhattan Project. The research conducted in the open science literature will trump research done in secret because of the benefits of collaboration and the free exchange of ideas.

While the United States should certainly increase funding for AI research, it should continue to treat it as an open scientific endeavor. Surveillance is not justified by the needs of machine learning, and real progress in AI doesn’t need it.

This essay was written with Jim Waldo, and previously appeared in Foreign Policy.

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Visiting the NSA

Yesterday, I visited the NSA. It was Cyber Command’s birthday, but that’s not why I was there. I visited as part of the Berklett Cybersecurity Project, run out of the Berkman Klein Center and funded by the Hewlett Foundation. (BERKman hewLETT — get it? We have a web page, but it’s badly out of date.)

It was a full day of meetings, all unclassified but under the Chatham House Rule. Gen. Nakasone welcomed us and took questions at the start. Various senior officials spoke with us on a variety of topics, but mostly focused on three areas:

  • Russian influence operations, both what the NSA and US Cyber Command did during the 2018 election and what they can do in the future;

  • China and the threats to critical infrastructure from untrusted computer hardware, both the 5G network and more broadly;

  • Machine learning, both how to ensure a ML system is compliant with all laws, and how ML can help with other compliance tasks.

It was all interesting. Those first two topics are ones that I am thinking and writing about, and it was good to hear their perspective. I find that I am much more closely aligned with the NSA about cybersecurity than I am about privacy, which made the meeting much less fraught than it would have been if we were discussing Section 702 of the FISA Amendments Act, Section 215 the USA Freedom Act (up for renewal next year), or any 4th Amendment violations. I don’t think we’re past those issues by any means, but they make up less of what I am working on.

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