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Nicholas Weaver on Cryptocurrencies

This is well-worth reading (non-paywalled version). Here’s the opening:

Cryptocurrencies, although a seemingly interesting idea, are simply not fit for purpose. They do not work as currencies, they are grossly inefficient, and they are not meaningfully distributed in terms of trust. Risks involving cryptocurrencies occur in four major areas: technical risks to participants, economic risks to participants, systemic risks to the cryptocurrency ecosystem, and societal risks.

I haven’t written much about cryptocurrencies, but I share Weaver’s skepticism.

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Google Login Security for High-Risk Users

Google has a new login service for high-risk users. it’s good, but unforgiving.

Logging in from a desktop will require a special USB key, while accessing your data from a mobile device will similarly require a Bluetooth dongle. All non-Google services and apps will be exiled from reaching into your Gmail or Google Drive. Google’s malware scanners will use a more intensive process to quarantine and analyze incoming documents. And if you forget your password, or lose your hardware login keys, you’ll have to jump through more hoops than ever to regain access, the better to foil any intruders who would abuse that process to circumvent all of Google’s other safeguards.

It’s called Advanced Protection.

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Digital Security Exchange: Security for High-Risk Communities

I am part of this very interesting project:

For many users, blog posts on how to install Signal, massive guides to protecting your digital privacy, and broad statements like “use Tor” — all offered in good faith and with the best of intentions — can be hard to understand or act upon. If we want to truly secure civil society from digital attacks and empower communities in their to fight to protect their rights, we’ve got to recognize that digital security is largely a human problem, not a technical one. Taking cues from the experiences of the deeply knowledgeable global digital security training community, the Digital Security Exchange will seek to make it easier for trainers and experts to connect directly to the communities in the U.S. — sharing expertise, documentation, and best practices — in order to increase capacity and security across the board.

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Buzzword Watch: Prosilience

Summer Fowler at CMU has invented a new word: prosilience:

I propose that we build operationally PROSILIENT organizations. If operational resilience, as we like to say, is risk management “all grown up,” then prosilience is resilience with consciousness of environment, self-awareness, and the capacity to evolve. It is not about being able to operate through disruption, it is about anticipating disruption and adapting before it even occurs–a proactive version of resilience. Nascent prosilient capabilities include exercises (tabletop or technical) that simulate how organizations would respond to a scenario. The goal, however, is to automate, expand, and perform continuous exercises based on real-world indicators rather than on scenarios.

I have long been a big fan of resilience as a security concept, and the property we should be aiming for. I’m not sure prosilience buys me anything new, but this is my first encounter with this new buzzword. It would certainly make for a best-selling business-book title.

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How the Media Influences Our Fear of Terrorism

Good article that crunches the data and shows that the press’s coverage of terrorism is disproportional to its comparative risk.

This isn’t new. I’ve written about it before, and wrote about it more generally when I wrote about the psychology of risk, fear, and security. Basically, the issue is the availability heuristic. We tend to infer the probability of something by how easy it is to bring examples of the thing to mind. So if we can think of a lot of tiger attacks in our community, we infer that the risk is high. If we can’t think of many lion attacks, we infer that the risk is low. But while this is a perfectly reasonable heuristic when living in small family groups in the East African highlands in 100,000 BC, it fails in the face of modern media. The media makes the rare seem more common by spending a lot of time talking about it. It’s not the media’s fault. By definition, news is “something that hardly ever happens.” But when the coverage of terrorist deaths exceeds the coverage of homicides, we have a tendency to mistakenly inflate the risk of the former while discount the risk of the latter.

Our brains aren’t very good at probability and risk analysis. We tend to exaggerate spectacular, strange and rare events, and downplay ordinary, familiar and common ones. We think rare risks are more common than they are. We fear them more than probability indicates we should.

There is a lot of psychological research that tries to explain this, but one of the key findings is this: People tend to base risk analysis more on stories than on data. Stories engage us at a much more visceral level, especially stories that are vivid, exciting or personally involving.

If a friend tells you about getting mugged in a foreign country, that story is more likely to affect how safe you feel traveling to that country than reading a page of abstract crime statistics will.

Novelty plus dread plus a good story equals overreaction.

It’s not just murders. It’s flying vs. driving: the former is much safer, but the latter is more spectacular when it occurs.

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Class Breaks

There’s a concept from computer security known as a class break. It’s a particular security vulnerability that breaks not just one system, but an entire class of systems. Examples might be a vulnerability in a particular operating system that allows an attacker to take remote control of every computer that runs on that system’s software. Or a vulnerability in Internet-enabled digital video recorders and webcams that allow an attacker to recruit those devices into a massive botnet.

It’s a particular way computer systems can fail, exacerbated by the characteristics of computers and software. It only takes one smart person to figure out how to attack the system. Once he does that, he can write software that automates his attack. He can do it over the Internet, so he doesn’t have to be near his victim. He can automate his attack so it works while he sleeps. And then he can pass the ability to someone­ — or to lots of people — ­without the skill. This changes the nature of security failures, and completely upends how we need to defend against them.

An example: Picking a mechanical door lock requires both skill and time. Each lock is a new job, and success at one lock doesn’t guarantee success with another of the same design. Electronic door locks, like the ones you now find in hotel rooms, have different vulnerabilities. An attacker can find a flaw in the design that allows him to create a key card that opens every door. If he publishes his attack software, not just the attacker, but anyone can now open every lock. And if those locks are connected to the Internet, attackers could potentially open door locks remotely — ­they could open every door lock remotely at the same time. That’s a class break.

It’s how computer systems fail, but it’s not how we think about failures. We still think about automobile security in terms of individual car thieves manually stealing cars. We don’t think of hackers remotely taking control of cars over the Internet. Or, remotely disabling every car over the Internet. We think about voting fraud as unauthorized individuals trying to vote. We don’t think about a single person or organization remotely manipulating thousands of Internet-connected voting machines.

In a sense, class breaks are not a new concept in risk management. It’s the difference between home burglaries and fires, which happen occasionally to different houses in a neighborhood over the course of the year, and floods and earthquakes, which either happen to everyone in the neighborhood or no one. Insurance companies can handle both types of risk, but they are inherently different. The increasing computerization of everything is moving us from a burglary/fire risk model to a flood/earthquake model, which a given threat either affects everyone in town or doesn’t happen at all.

But there’s a key difference between floods/earthquakes and class breaks in computer systems: the former are random natural phenomena, while the latter is human-directed. Floods don’t change their behavior to maximize their damage based on the types of defenses we build. Attackers do that to computer systems. Attackers examine our systems, looking for class breaks. And once one of them finds one, they’ll exploit it again and again until the vulnerability is fixed.

As we move into the world of the Internet of Things, where computers permeate our lives at every level, class breaks will become increasingly important. The combination of automation and action at a distance will give attackers more power and leverage than they have ever had before. Security notions like the precautionary principle­ — where the potential of harm is so great that we err on the side of not deploying a new technology without proofs of security — will become more important in a world where an attacker can open all of the door locks or hack all of the power plants. It’s not an inherently less secure world, but it’s a differently secure world. It’s a world where driverless cars are much safer than people-driven cars, until suddenly they’re not. We need to build systems that assume the possibility of class breaks — and maintain security despite them.

This essay originally appeared on Edge.org as part of their annual question. This year it was: “What scientific term or concept ought to be more widely known?

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Confusing Security Risks with Moral Judgments

Interesting research that shows we exaggerate the risks of something when we find it morally objectionable.

From an article about and interview with the researchers:

To get at this question experimentally, Thomas and her collaborators created a series of vignettes in which a parent left a child unattended for some period of time, and participants indicated the risk of harm to the child during that period. For example, in one vignette, a 10-month-old was left alone for 15 minutes, asleep in the car in a cool, underground parking garage. In another vignette, an 8-year-old was left for an hour at a Starbucks, one block away from her parent’s location.

To experimentally manipulate participants’ moral attitude toward the parent, the experimenters varied the reason the child was left unattended across a set of six experiments with over 1,300 online participants. In some cases, the child was left alone unintentionally (for example, in one case, a mother is hit by a car and knocked unconscious after buckling her child into her car seat, thereby leaving the child unattended in the car seat). In other cases, the child was left unattended so the parent could go to work, do some volunteering, relax or meet a lover.

Not surprisingly, the parent’s reason for leaving a child unattended affected participants’ judgments of whether the parent had done something immoral: Ratings were over 3 on a 10-point scale even when the child was left unattended unintentionally, but they skyrocketed to nearly 8 when the parent left to meet a lover. Ratings for the other cases fell in between.

The more surprising result was that perceptions of risk followed precisely the same pattern. Although the details of the cases were otherwise the same -­ that is, the age of the child, the duration and location of the unattended period, and so on -­ participants thought children were in significantly greater danger when the parent left to meet a lover than when the child was left alone unintentionally. The ratings for the other cases, once again, fell in between. In other words, participants’ factual judgments of how much danger the child was in while the parent was away varied according to the extent of their moral outrage concerning the parent’s reason for leaving.

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