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What Will It Take?

What will it take for policy makers to take cybersecurity seriously? Not minimal-change seriously. Not here-and-there seriously. But really seriously. What will it take for policy makers to take cybersecurity seriously enough to enact substantive legislative changes that would address the problems? It’s not enough for the average person to be afraid of cyberattacks. They need to know that there are engineering fixes—and that’s something we can provide.

For decades, I have been waiting for the “big enough” incident that would finally do it. In 2015, Chinese military hackers hacked the Office of Personal Management and made off with the highly personal information of about 22 million Americans who had security clearances. In 2016, the Mirai botnet leveraged millions of Internet-of-Things devices with default admin passwords to launch a denial-of-service attack that disabled major Internet platforms and services in both North America and Europe. In 2017, hackers—years later we learned that it was the Chinese military—hacked the credit bureau Equifax and stole the personal information of 147 million Americans. In recent years, ransomware attacks have knocked hospitals offline, and many articles have been written about Russia inside the U.S. power grid. And last year, the Russian SVR hacked thousands of sensitive networks inside civilian critical infrastructure worldwide in what we’re now calling Sunburst (and used to call SolarWinds).

Those are all major incidents to security people, but think about them from the perspective of the average person. Even the most spectacular failures don’t affect 99.9% of the country. Why should anyone care if the Chinese have his or her credit records? Or if the Russians are stealing data from some government network? Few of us have been directly affected by ransomware, and a temporary Internet outage is just temporary.

Cybersecurity has never been a campaign issue. It isn’t a topic that shows up in political debates. (There was one question in a 2016 Clinton–Trump debate, but the response was predictably unsubstantive.) This just isn’t an issue that most people prioritize, or even have an opinion on.

So, what will it take? Many of my colleagues believe that it will have to be something with extreme emotional intensity—sensational, vivid, salient—that results in large-scale loss of life or property damage. A successful attack that actually poisons a water supply, as someone tried to do in January by raising the levels of lye at a Florida water-treatment plant. (That one was caught early.) Or an attack that disables Internet-connected cars at speed, something that was demonstrated by researchers in 2014. Or an attack on the power grid, similar to what Russia did to the Ukraine in 2015 and 2016. Will it take gas tanks exploding and planes falling out of the sky for the average person to read about the casualties and think “that could have been me”?

Here’s the real problem. For the average nonexpert—and in this category I include every lawmaker—to push for change, they not only need to believe that the present situation is intolerable, they also need to believe that an alternative is possible. Real legislative change requires a belief that the never-ending stream of hacks and attacks is not inevitable, that we can do better. And that will require creating working examples of secure, dependable, resilient systems.

Providing alternatives is how engineers help facilitate social change. We could never have eliminated sales of tungsten-filament household light bulbs if fluorescent and LED replacements hadn’t become available. Reducing the use of fossil fuel for electricity generation requires working wind turbines and cost-effective solar cells.

We need to demonstrate that it’s possible to build systems that can defend themselves against hackers, criminals, and national intelligence agencies; secure Internet-of-Things systems; and systems that can reestablish security after a breach. We need to prove that hacks aren’t inevitable, and that our vulnerability is a choice. Only then can someone decide to choose differently. When people die in a cyberattack and everyone asks “What can be done?” we need to have something to tell them.

We don’t yet have the technology to build a truly safe, secure, and resilient Internet and the computers that connect to it. Yes, we have lots of security technologies. We have older secure systems—anyone still remember Apollo’s DomainOS and MULTICS?—that lost out in a market that didn’t reward security. We have newer research ideas and products that aren’t successful because the market still doesn’t reward security. We have even newer research ideas that won’t be deployed, again, because the market still prefers convenience over security.

What I am proposing is something more holistic, an engineering research task on a par with the Internet itself. The Internet was designed and built to answer this question: Can we build a reliable network out of unreliable parts in an unreliable world? It turned out the answer was yes, and the Internet was the result. I am asking a similar research question: Can we build a secure network out of insecure parts in an insecure world? The answer isn’t obviously yes, but it isn’t obviously no, either.

While any successful demonstration will include many of the security technologies we know and wish would see wider use, it’s much more than that. Creating a secure Internet ecosystem goes beyond old-school engineering to encompass the social sciences. It will include significant economic, institutional, and psychological considerations that just weren’t present in the first few decades of Internet research.

Cybersecurity isn’t going to get better until the economic incentives change, and that’s not going to change until the political incentives change. The political incentives won’t change until there is political liability that comes from voter demands. Those demands aren’t going to be solely the results of insecurity. They will also be the result of believing that there’s a better alternative. It is our task to research, design, build, test, and field that better alternative—even though the market couldn’t care less right now.

This essay originally appeared in the May/June 2021 issue of IEEE Security & Privacy. I forgot to publish it here.

Posted on February 14, 2023 at 7:06 AM • View Comments

Hacking the Tax Code

The tax code isn’t software. It doesn’t run on a computer. But it’s still code. It’s a series of algorithms that takes an input—financial information for the year—and produces an output: the amount of tax owed. It’s incredibly complex code; there are a bazillion details and exceptions and special cases. It consists of government laws, rulings from the tax authorities, judicial decisions, and legal opinions.

Like computer code, the tax code has bugs. They might be mistakes in how the tax laws were written. They might be mistakes in how the tax code is interpreted, oversights in how parts of the law were conceived, or unintended omissions of some sort or another. They might arise from the exponentially huge number of ways different parts of the tax code interact.

A recent example comes from the 2017 Tax Cuts and Jobs Act. That law was drafted in both haste and secret, and quickly passed without any time for review—or even proofreading. One of the things in it was a typo that accidentally categorized military death benefits as earned income. The practical effect of that mistake is that surviving family members were hit with surprise tax bills of US$10,000 or more.

That’s a bug, but not a vulnerability. An example of a vulnerability is the “Double Irish with a Dutch Sandwich.” It arises from the interactions of tax laws in multiple countries, and it’s how companies like Google and Apple have avoided paying U.S. taxes despite being U.S. companies. Estimates are that U.S. companies avoided paying nearly US$200 billion in taxes in 2017 alone.

In the tax world, vulnerabilities are called loopholes. Exploits are called tax avoidance strategies. And there are thousands of black-hat researchers who examine every line of the tax code looking for exploitable vulnerabilities—tax attorneys and tax accountants.

Some vulnerabilities are deliberately created. Lobbyists are constantly trying to insert this or that provision into the tax code that benefits their clients financially. That same 2017 U.S. tax law included a special tax break for oil and gas investment partnerships, a special exemption that ensures that fewer than 1 in 1,000 estates will have to pay estate tax, and language specifically expanding a pass-through loophole that industry uses to incorporate companies offshore and avoid U.S. taxes. That’s not hacking the tax code. It’s hacking the processes that create them: the legislative process that creates tax law.

We know the processes to use to fix vulnerabilities in computer code. Before the code is finished, we can employ some sort of secure development processes, with automatic bug-finding tools and maybe source code audits. After the code is deployed, we might rely on vulnerability finding by the security community, perhaps bug bounties—and most of all, quick patching when vulnerabilities are discovered.

What does it mean to “patch” the tax code? Passing any tax legislation is a big deal, especially in the United States where the issue is so partisan and contentious. (That 2017 earned income tax bug for military families hasn’t yet been fixed. And that’s an easy one; everyone acknowledges it was a mistake.) We don’t have the ability to patch tax code with anywhere near the same agility that we have to patch software.

We can patch some vulnerabilities, though. The other way tax code is modified is by IRS and judicial rulings. The 2017 tax law capped income tax deductions for property taxes. This provision didn’t come into force in 2018, so someone came up with the clever hack to prepay 2018 property taxes in 2017. Just before the end of the year, the IRS ruled about when that was legal and when it wasn’t. Short answer: most of the time, it wasn’t.

There’s another option: that the vulnerability isn’t patched and isn’t explicitly approved, and slowly becomes part of the normal way of doing things. Lots of tax loopholes end up like this. Sometimes they’re even given retroactive legality by the IRS or Congress after a constituency and lobbying effort gets behind them. This process is how systems evolve. A hack subverts the intent of a system. Whatever governing system has jurisdiction either blocks the hack or allows it—or does nothing and the hack becomes the new normal.

Here’s my question: what happens when artificial intelligence and machine learning (ML) gets hold of this problem? We already have ML systems that find software vulnerabilities. What happens when you feed a ML system the entire U.S. tax code and tell it to figure out all of the ways to minimize the amount of tax owed? Or, in the case of a multinational corporation, to feed it the entire planet’s tax codes? What sort of vulnerabilities would it find? And how many? Dozens or millions?

In 2015, Volkswagen was caught cheating on emissions control tests. It didn’t forge test results; it got the cars’ computers to cheat for them. Engineers programmed the software in the car’s onboard computer to detect when the car was undergoing an emissions test. The computer then activated the car’s emissions-curbing systems, but only for the duration of the test. The result was that the cars had much better performance on the road at the cost of producing more pollution.

ML will result in lots of hacks like this. They’ll be more subtle. They’ll be even harder to discover. It’s because of the way ML systems optimize themselves, and because their specific optimizations can be impossible for us humans to understand. Their human programmers won’t even know what’s going on.

Any good ML system will naturally find and exploit hacks. This is because their only constraints are the rules of the system. If there are problems, inconsistencies, or loopholes in the rules, and if those properties lead to a “better” solution as defined by the program, then those systems will find them. The challenge is that you have to define the system’s goals completely and precisely, and that that’s impossible.

The tax code can be hacked. Financial markets regulations can be hacked. The market economy, democracy itself, and our cognitive systems can all be hacked. Tasking a ML system to find new hacks against any of these is still science fiction, but it’s not stupid science fiction. And ML will drastically change how we need to think about policy, law, and government. Now’s the time to figure out how.

This essay originally appeared in the September/October 2020 issue of IEEE Security & Privacy. I wrote it when I started writing my latest book, but never published it here.

Posted on February 10, 2023 at 6:24 AM • View Comments

AI and Political Lobbying

Launched just weeks ago, ChatGPT is already threatening to upend how we draft everyday communications like emails, college essays and myriad other forms of writing.

Created by the company OpenAI, ChatGPT is a chatbot that can automatically respond to written prompts in a manner that is sometimes eerily close to human.

But for all the consternation over the potential for humans to be replaced by machines in formats like poetry and sitcom scripts, a far greater threat looms: artificial intelligence replacing humans in the democratic processes—not through voting, but through lobbying.

ChatGPT could automatically compose comments submitted in regulatory processes. It could write letters to the editor for publication in local newspapers. It could comment on news articles, blog entries and social media posts millions of times every day. It could mimic the work that the Russian Internet Research Agency did in its attempt to influence our 2016 elections, but without the agency’s reported multimillion-dollar budget and hundreds of employees.

Automatically generated comments aren’t a new problem. For some time, we have struggled with bots, machines that automatically post content. Five years ago, at least a million automatically drafted comments were believed to have been submitted to the Federal Communications Commission regarding proposed regulations on net neutrality. In 2019, a Harvard undergraduate, as a test, used a text-generation program to submit 1,001 comments in response to a government request for public input on a Medicaid issue. Back then, submitting comments was just a game of overwhelming numbers.

Platforms have gotten better at removing “coordinated inauthentic behavior.” Facebook, for example, has been removing over a billion fake accounts a year. But such messages are just the beginning. Rather than flooding legislators’ inboxes with supportive emails, or dominating the Capitol switchboard with synthetic voice calls, an AI system with the sophistication of ChatGPT but trained on relevant data could selectively target key legislators and influencers to identify the weakest points in the policymaking system and ruthlessly exploit them through direct communication, public relations campaigns, horse trading or other points of leverage.

When we humans do these things, we call it lobbying. Successful agents in this sphere pair precision message writing with smart targeting strategies. Right now, the only thing stopping a ChatGPT-equipped lobbyist from executing something resembling a rhetorical drone warfare campaign is a lack of precision targeting. AI could provide techniques for that as well.

A system that can understand political networks, if paired with the textual-generation capabilities of ChatGPT, could identify the member of Congress with the most leverage over a particular policy area—say, corporate taxation or military spending. Like human lobbyists, such a system could target undecided representatives sitting on committees controlling the policy of interest and then focus resources on members of the majority party when a bill moves toward a floor vote.

Once individuals and strategies are identified, an AI chatbot like ChatGPT could craft written messages to be used in letters, comments—anywhere text is useful. Human lobbyists could also target those individuals directly. It’s the combination that’s important: Editorial and social media comments only get you so far, and knowing which legislators to target isn’t itself enough.

This ability to understand and target actors within a network would create a tool for AI hacking, exploiting vulnerabilities in social, economic and political systems with incredible speed and scope. Legislative systems would be a particular target, because the motive for attacking policymaking systems is so strong, because the data for training such systems is so widely available and because the use of AI may be so hard to detect—particularly if it is being used strategically to guide human actors.

The data necessary to train such strategic targeting systems will only grow with time. Open societies generally make their democratic processes a matter of public record, and most legislators are eager—at least, performatively so—to accept and respond to messages that appear to be from their constituents.

Maybe an AI system could uncover which members of Congress have significant sway over leadership but still have low enough public profiles that there is only modest competition for their attention. It could then pinpoint the SuperPAC or public interest group with the greatest impact on that legislator’s public positions. Perhaps it could even calibrate the size of donation needed to influence that organization or direct targeted online advertisements carrying a strategic message to its members. For each policy end, the right audience; and for each audience, the right message at the right time.

What makes the threat of AI-powered lobbyists greater than the threat already posed by the high-priced lobbying firms on K Street is their potential for acceleration. Human lobbyists rely on decades of experience to find strategic solutions to achieve a policy outcome. That expertise is limited, and therefore expensive.

AI could, theoretically, do the same thing much more quickly and cheaply. Speed out of the gate is a huge advantage in an ecosystem in which public opinion and media narratives can become entrenched quickly, as is being nimble enough to shift rapidly in response to chaotic world events.

Moreover, the flexibility of AI could help achieve influence across many policies and jurisdictions simultaneously. Imagine an AI-assisted lobbying firm that can attempt to place legislation in every single bill moving in the US Congress, or even across all state legislatures. Lobbying firms tend to work within one state only, because there are such complex variations in law, procedure and political structure. With AI assistance in navigating these variations, it may become easier to exert power across political boundaries.

Just as teachers will have to change how they give students exams and essay assignments in light of ChatGPT, governments will have to change how they relate to lobbyists.

To be sure, there may also be benefits to this technology in the democracy space; the biggest one is accessibility. Not everyone can afford an experienced lobbyist, but a software interface to an AI system could be made available to anyone. If we’re lucky, maybe this kind of strategy-generating AI could revitalize the democratization of democracy by giving this kind of lobbying power to the powerless.

However, the biggest and most powerful institutions will likely use any AI lobbying techniques most successfully. After all, executing the best lobbying strategy still requires insiders—people who can walk the halls of the legislature—and money. Lobbying isn’t just about giving the right message to the right person at the right time; it’s also about giving money to the right person at the right time. And while an AI chatbot can identify who should be on the receiving end of those campaign contributions, humans will, for the foreseeable future, need to supply the cash. So while it’s impossible to predict what a future filled with AI lobbyists will look like, it will probably make the already influential and powerful even more so.

This essay was written with Nathan Sanders, and previously appeared in the New York Times.

Edited to Add: After writing this, we discovered that a research group is researching AI and lobbying:

We used autoregressive large language models (LLMs, the same type of model behind the now wildly popular ChatGPT) to systematically conduct the following steps. (The full code is available at this GitHub link: https://github.com/JohnNay/llm-lobbyist.)

Summarize official U.S. Congressional bill summaries that are too long to fit into the context window of the LLM so the LLM can conduct steps 2 and 3.

Using either the original official bill summary (if it was not too long), or the summarized version:

Assess whether the bill may be relevant to a company based on a company’s description in its SEC 10K filing.

Provide an explanation for why the bill is relevant or not.

Provide a confidence level to the overall answer.

If the bill is deemed relevant to the company by the LLM, draft a letter to the sponsor of the bill arguing for changes to the proposed legislation.

Here is the paper.

EDITED TO ADD (9/12): Emily Bender has a critique of this essay.

Posted on January 18, 2023 at 7:19 AM • View Comments

Decarbonizing Cryptocurrencies through Taxation

Maintaining bitcoin and other cryptocurrencies causes about 0.3 percent of global CO₂ emissions. That may not sound like a lot, but it’s more than the emissions of Switzerland, Croatia, and Norway combined. As many cryptocurrencies crash and the FTX bankruptcy moves into the litigation stage, regulators are likely to scrutinize the cryptocurrency world more than ever before. This presents a perfect opportunity to curb their environmental damage.

The good news is that cryptocurrencies don’t have to be carbon intensive. In fact, some have near-zero emissions. To encourage polluting currencies to reduce their carbon footprint, we need to force buyers to pay for their environmental harms through taxes.

The difference in emissions among cryptocurrencies comes down to how they create new coins. Bitcoin and other high emitters use a system called “proof of work“: to generate coins, participants, or “miners,” have to solve math problems that demand extraordinary computing power. This allows currencies to maintain their decentralized ledger—the blockchain—but requires enormous amounts of energy.

Greener alternatives exist. Most notably, the “proof of stake” system enables participants to maintain their blockchain by depositing cryptocurrency holdings in a pool. When the second-largest cryptocurrency, Ethereum, switched from proof of work to proof of stake earlier this year, its energy consumption dropped by more than 99.9% overnight.

Bitcoin and other cryptocurrencies probably won’t follow suit unless forced to, because proof of work offers massive profits to miners—and they’re the ones with power in the system. Multiple legislative levers could be used to entice them to change.

The most blunt solution is to ban cryptocurrency mining altogether. China did this in 2018, but it only made the problem worse; mining moved to other countries with even less efficient energy generation, and emissions went up. The only way for a mining ban to meaningfully reduce carbon emissions is to enact it across most of the globe. Achieving that level of international consensus is, to say the least, unlikely.

A second solution is to prohibit the buying and selling of proof-of-work currencies. The European Parliament’s Committee on Economic and Monetary Affairs considered making such a proposal, but voted against it in March. This is understandable; as with a mining ban, it would be both viewed as paternalistic and difficult to implement politically.

Employing a tax instead of an outright ban would largely skirt these issues. As with taxes on gasoline, tobacco, plastics, and alcohol, a cryptocurrency tax could reduce real-world harm by making consumers pay for it.

Most ways of taxing cryptocurrencies would be inefficient, because they’re easy to circumvent and hard to enforce. To avoid these pitfalls, the tax should be levied as a fixed percentage of each proof-of-work-cryptocurrency purchase. Cryptocurrency exchanges should collect the tax, just as merchants collect sales taxes from customers before passing the sum on to governments. To make it harder to evade, the tax should apply regardless of how the proof-of-work currency is being exchanged—whether for a fiat currency or another cryptocurrency. Most important, any state that implements the tax should target all purchases by citizens in its jurisdiction, even if they buy through exchanges with no legal presence in the country.

This sort of tax would be transparent and easy to enforce. Because most people buy cryptocurrencies from one of only a few large exchanges—such as Binance, Coinbase, and Kraken—auditing them should be cheap enough that it pays for itself. If an exchange fails to comply, it should be banned.

Even a small tax on proof-of-work currencies would reduce their damage to the planet. Imagine that you’re new to cryptocurrency and want to become a first-time investor. You’re presented with a range of currencies to choose from: bitcoin, ether, litecoin, monero, and others. You notice that all of them except ether add an environmental tax to your purchase price. Which one do you buy?

Countries don’t need to coordinate across borders for a proof-of-work tax on their own citizens to be effective. But early adopters should still consider ways to encourage others to come on board. This has precedent. The European Union is trying to influence global policy with its carbon border adjustments, which are designed to discourage people from buying carbon-intensive products abroad in order to skirt taxes. Similar rules for a proof-of-work tax could persuade other countries to adopt one.

Of course, some people will try to evade the tax, just as people evade every other tax. For example, people might buy tax-free coins on centralized exchanges and then swap them for polluting coins on decentralized exchanges. To some extent, this is inevitable; no tax is perfect. But the effort and technical know-how needed to evade a proof-of-work tax will be a major deterrent.

Even if only a few countries implement this tax—and even if some people evade it—the desirability of bitcoin will fall globally, and the environmental benefit will be significant. A high enough tax could also cause a self-reinforcing cycle that will drive down these cryptocurrencies’ prices. Because the value of many cryptocurrencies rely largely on speculation, they are dependent on future buyers. When speculators are deterred by the tax, the lack of demand will cause the price of bitcoin to fall, which could prompt more current holders to sell—further lowering prices and accelerating the effect. Declining prices will pressure the bitcoin community to abandon proof of work altogether.

Taxing proof-of-work exchanges might hurt them in the short run, but it would not hinder blockchain innovation. Instead, it would redirect innovation toward greener cryptocurrencies. This is no different than how government incentives for electric vehicles encourage carmakers to improve green alternatives to the internal combustion engine. These incentives don’t restrict innovation in automobiles—they promote it.

Taxing environmentally harmful cryptocurrencies can gain support across the political spectrum, from people with varied interests. It would benefit blockchain innovators and cryptocurrency researchers by shifting focus from environmental harm to beneficial uses of the technology. It has the potential to make our planet significantly greener. It would increase government revenues.

Even bitcoin maximalists have reason to embrace the proposal: it would offer the bitcoin community a chance to prove it can survive and grow sustainably.

This essay was written with Christos Porios, and previously appeared in the Atlantic.

Posted on January 4, 2023 at 7:17 AM • View Comments

Hidden Anti-Cryptography Provisions in Internet Anti-Trust Bills

Two bills attempting to reduce the power of Internet monopolies are currently being debated in Congress: S. 2992, the American Innovation and Choice Online Act; and S. 2710, the Open App Markets Act. Reducing the power to tech monopolies would do more to “fix” the Internet than any other single action, and I am generally in favor of them both. (The Center for American Progress wrote a good summary and evaluation of them. I have written in support of the bill that would force Google and Apple to give up their monopolies on their phone app stores.)

There is a significant problem, though. Both bills have provisions that could be used to break end-to-end encryption.

Let’s start with S. 2992. Sec. 3(c)(7)(A)(iii) would allow a company to deny access to apps installed by users, where those app makers “have been identified [by the Federal Government] as national security, intelligence, or law enforcement risks.” That language is far too broad. It would allow Apple to deny access to an encryption service provider that provides encrypted cloud backups to the cloud (which Apple does not currently offer). All Apple would need to do is point to any number of FBI materials decrying the security risks with “warrant proof encryption.”

Sec. 3(c)(7)(A)(vi) states that there shall be no liability for a platform “solely” because it offers “end-to-end encryption.” This language is too narrow. The word “solely” suggests that offering end-to-end encryption could be a factor in determining liability, provided that it is not the only reason. This is very similar to one of the problems with the encryption carve-out in the EARN IT Act. The section also doesn’t mention any other important privacy-protective features and policies, which also shouldn’t be the basis for creating liability for a covered platform under Sec. 3(a).

In Sec. 2(a)(2), the definition of business user excludes any person who “is a clear national security risk.” This term is undefined, and as such far too broad. It can easily be interpreted to cover any company that offers an end-to-end encrypted alternative, or a service offered in a country whose privacy laws forbid disclosing data in response to US court-ordered surveillance. Again, the FBI’s repeated statements about end-to-end encryption could serve as support.

Finally, under Sec. 3(b)(2)(B), platforms have an affirmative defense for conduct that would otherwise violate the Act if they do so in order to “protect safety, user privacy, the security of nonpublic data, or the security of the covered platform.” This language is too vague, and could be used to deny users the ability to use competing services that offer better security/privacy than the incumbent platform—particularly where the platform offers subpar security in the name of “public safety.” For example, today Apple only offers unencrypted iCloud backups, which it can then turn over governments who claim this is necessary for “public safety.” Apple can raise this defense to justify its blocking third-party services from offering competing, end-to-end encrypted backups of iMessage and other sensitive data stored on an iPhone.

S. 2710 has similar problems. Sec 7. (6)(B) contains language specifying that the bill does not “require a covered company to interoperate or share data with persons or business users that…have been identified by the Federal Government as national security, intelligence, or law enforcement risks.” This would mean that Apple could ignore the prohibition against private APIs, and deny access to otherwise private APIs, for developers of encryption products that have been publicly identified by the FBI. That is, end-to-end encryption products.

I want those bills to pass, but I want those provisions cleared up so we don’t lose strong end-to-end encryption in our attempt to reign in the tech monopolies.

EDITED TO ADD (6/23): A few DC insiders have responded to me about this post. Their basic point is this: “Your threat model is wrong. The big tech companies can already break end-to-end encryption if they want. They don’t need any help, and this bill doesn’t give the FBI any new leverage they don’t already have. This bill doesn’t make anything any worse than it is today.” That’s a reasonable response. These bills are definitely a net positive for humanity.

Posted on June 21, 2022 at 6:34 AM • View Comments

Smartphones and Civilians in Wartime

Interesting article about civilians using smartphones to assist their militaries in wartime, and how that blurs the important legal distinction between combatants and non-combatants:

The principle of distinction between the two roles is a critical cornerstone of international humanitarian law—the law of armed conflict, codified by decades of customs and laws such as the Geneva Conventions. Those considered civilians and civilian targets are not to be attacked by military forces; as they are not combatants, they should be spared. At the same time, they also should not act as combatants—if they do, they may lose this status.

The conundrum, then, is how to classify a civilian who, with the use of their smartphone, potentially becomes an active participant in a military sensor system. (To be clear, solely having the app installed is not sufficient to lose the protected status. What matters is actual usage.) The Additional Protocol I to Geneva Conventions states that civilians enjoy protection from the “dangers arising from military operations unless and for such time as they take a direct part in hostilities.” Legally, if civilians engage in military activity, such as taking part in hostilities by using weapons, they forfeit their protected status, “for such time as they take a direct part in hostilities” that “affect[s] the military operations,” according to the International Committee of the Red Cross, the traditional impartial custodian of International Humanitarian Law. This is the case even if the people in question are not formally members of the armed forces. By losing the status of a civilian, one may become a legitimate military objective, carrying the risk of being directly attacked by military forces.

Posted on June 9, 2022 at 6:22 AM • View Comments

US Critical Infrastructure Companies Will Have to Report When They Are Hacked

This will be law soon:

Companies critical to U.S. national interests will now have to report when they’re hacked or they pay ransomware, according to new rules approved by Congress.

[…]

The reporting requirement legislation was approved by the House and the Senate on Thursday and is expected to be signed into law by President Joe Biden soon. It requires any entity that’s considered part of the nation’s critical infrastructure, which includes the finance, transportation and energy sectors, to report any “substantial cyber incident” to the government within three days and any ransomware payment made within 24 hours.

Even better would be if they had to report it to the public.

Posted on March 15, 2022 at 6:01 AM • View Comments

Me on App Store Monopolies and Security

There are two bills working their way through Congress that would force companies like Apple to allow competitive app stores. Apple hates this, since it would break its monopoly, and it’s making a variety of security arguments to bolster its argument. I have written a rebuttal:

I would like to address some of the unfounded security concerns raised about these bills. It’s simply not true that this legislation puts user privacy and security at risk. In fact, it’s fairer to say that this legislation puts those companies’ extractive business-models at risk. Their claims about risks to privacy and security are both false and disingenuous, and motivated by their own self-interest and not the public interest. App store monopolies cannot protect users from every risk, and they frequently prevent the distribution of important tools that actually enhance security. Furthermore, the alleged risks of third-party app stores and “side-loading” apps pale in comparison to their benefits. These bills will encourage competition, prevent monopolist extortion, and guarantee users a new right to digital self-determination.

Matt Stoller has also written about this.

EDITED TO ADD (2/13): Here are the two bills.

Posted on February 1, 2022 at 2:26 PM • View Comments

The Problem with Treating Data as a Commodity

Excellent Brookings paper: “Why data ownership is the wrong approach to protecting privacy.”

From the introduction:

Treating data like it is property fails to recognize either the value that varieties of personal information serve or the abiding interest that individuals have in their personal information even if they choose to “sell” it. Data is not a commodity. It is information. Any system of information rights—whether patents, copyrights, and other intellectual property, or privacy rights—presents some tension with strong interest in the free flow of information that is reflected by the First Amendment. Our personal information is in demand precisely because it has value to others and to society across a myriad of uses.

From the conclusion:

Privacy legislation should empower individuals through more layered and meaningful transparency and individual rights to know, correct, and delete personal information in databases held by others. But relying entirely on individual control will not do enough to change a system that is failing individuals, and trying to reinforce control with a property interest is likely to fail society as well. Rather than trying to resolve whether personal information belongs to individuals or to the companies that collect it, a baseline federal privacy law should directly protect the abiding interest that individuals have in that information and also enable the social benefits that flow from sharing information.

Posted on February 26, 2021 at 6:28 AM • View Comments