February 26, 2015

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Cyberterrorism or Cybervandalism?

When hackers believed by the U.S. government to have been sponsored by the state of North Korea infiltrated Sony Pictures’ corporate network and leaked reams of sensitive documents, the act was quickly labeled an act of “cyberterrorism.” When hackers claiming to be affiliated with ISIS subsequently hijacked the YouTube and Twitter accounts of the U.S. military’s Central Command, military officials called it an act of “cybervandalism.” A third category of cyberattack, which presents definitional challenges of its own, is “cyberwarfare.” In terms of the nature and scale of any official response, it obviously matters quite a lot which bucket the government and the media choose when they categorize a cyberattack to the public. So how is that choice made as a descriptive matter? And how should it be made?

It seems to me that there are several potentially relevant factors to assess when drawing the semantic line between cyberterrorism and cybervandalism. The ones that spring to mind are the origin of the attack (e.g., state-sponsored v. state-aligned v. unaligned); the target of the attack (e.g., public infrastructure v. corporate infrastructure; critical infrastructure—however defined—v. non-critical infrastructure); the nature of the harm caused (e.g., personal injury v. injury to property); and the reach and severity of the harm caused (e.g., minor or major; isolated v. pervasive). Are these the right factors to take into account? If so, what configuration of factors makes a cyberattack an act of cyberterrorism as opposed to an act cybervandalism? And how should we distinguish both cyberterrorism and cybervandalism from cyberwarfare? Is cyberwarfare only state-to-state?

As the Internet is increasingly beset by attacks of all kinds from all quarters in the name of all different ideologies (or just lulz), it seems vital to have in place a stable, rational way of classifying cyberattacks so that official responses can be appropriate and proportional. I know there are a lot of cybersecurity experts who read FTT. I am definitely not one. I’d love to hear your thoughts about a principled taxonomy for cyberattacks. If there’s a good article about this out there somewhere, I’d be happy to get the citation.

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Consensus in Bitcoin: One system, many models

At a technical level, the Bitcoin protocol is a clever solution to the consensus problem in computer science. The idea of consensus is very general — a number of participants together execute a computation to come to agreement about the state of the world, or a subset of it that they’re interested in.

Because of this generality, there are different methods for analyzing and proving things about such consensus protocols, coming from different areas of applied math and computer science. These methods use different languages and terminology and embody different assumptions and views. As a result, they’re not always consistent with each other. This is a recipe for confusion; often people disagree because they’ve implicitly assumed one world-view or another. In this post I’ll explain the two main sets of models that are used to analyze the security of consensus in Bitcoin.

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On the Sony Pictures Security Breach

The recent security breach at Sony Pictures is one of the most embarrassing breaches ever, though not the most technically sophisticated. The incident raises lots of interesting questions about the current state of security and public policy.
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How cookies can be used for global surveillance

Today we present an updated version of our paper examining how the ubiquitous use of online tracking cookies can allow an adversary conducting network surveillance to target a user or surveil users en masse. In the initial version of the study, summarized below, we examined the technical feasibility of the attack. Now we’ve made the attack model more complete and nuanced as well as analyzed the effectiveness of several browser privacy tools in preventing the attack. Finally, inspired by Jonathan Mayer and Ed Felten’s The Web is Flat study, we incorporate the geographic topology of the Internet into our measurements of simulated web traffic and our adversary model, providing a more realistic view of how effective this attack is in practice. [Read more...]

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Why ASICs may be good for Bitcoin

Bitcoin mining is now almost exclusively performed by Bitcoin-specific ASICs (application-specific integrated circuits). These chips are made by a few startup manufacturers and cannot be used for anything else besides mining Bitcoin or closely related cryptocurrencies [1]. Because they are somewhere between a thousand and a million times more efficient at mining Bitcoin than a general-purpose computer that you can buy for the same price, they have quickly become the only game in town.

Many have lamented the rise of ASICs, feeling it departs from the democratic “one computer, one vote” vision laid out by Satoshi Nakamoto in the original Bitcoin design. There is also significant concern that mining is now too centralized, driven by ASICs as well as the rise of mining pools. Because of this, there have been many efforts to design “ASIC-resistant” mining puzzles. One of the earliest alternatives to Bitcoin, Litecoin, chose the memory-hard scrypt instead of SHA-256 in the hope of preventing ASIC mining. Despite this, there are now ASICs for mining Litecoin and their speedup over general-purpose computers may be even greater than that of Bitcoin ASICs. Litecoin’s developers themselves have essentially given up on the principle of ASIC-resistance. Subsequent efforts have included X11, which combines eleven hash functions to attempt to make ASICs difficult to build, but it’s probably only a matter of time before X11 ASICs arise as well. It’s been convincingly argued that ASIC-resistance is probably impossible in the long-term, so we should all accept that ASICs are inevitable in a successful cryptocurrency.

I would like to expand on the argument  here though by positing that ASICs may actually make Bitcoin (and similar cryptocurrencies) more stable by ensuring that miners have a large sunk cost and depend on future mining revenues to recoup it. Even if it were technically possible to design a perfectly ASIC-resistant mining puzzle which ensured that mining was efficient on general-purpose computers, this might be a bad idea if it meant you could obtain a lot of computational capacity and use it in a destructive attack on Bitcoin without significantly devaluing your computational resources’ value. [Read more...]

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Striking a balance between advertising and ad blocking

In the news, we have a consortium of French publishers, which somehow includes several major U.S. corporations (Google, Microsoft), attempting to sue AdBlock Plus developer Eyeo, a German firm with developers around the world. I have no idea of the legal basis for their case, but it’s all about the money. AdBlock Plus and the closely related AdBlock are among the most popular Chrome extensions, by far, and publishers will no doubt claim huge monetary damages around presumed “lost income”.
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Expert Panel Report: A New Governance Model for Communications Security?

Today, the vulnerable state of electronic communications security dominates headlines across the globe, while surveillance, money and power increasingly permeate the ‘cybersecurity’ policy arena. With the stakes so high, how should communications security be regulated? Deirdre Mulligan (UC Berkeley), Ashkan Soltani (independent, Washington Post), Ian Brown (Oxford) and Michel van Eeten (TU Delft) weighed in on this proposition at an expert panel on my doctoral project at the Amsterdam Information Influx conference. [Read more...]

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CITP Call for Fellows, Postdocs, and Visiting Professor for 2015-16

The Center for Information Technology Policy is an interdisciplinary research center at Princeton that sits at the crossroads of engineering, the social sciences, law, and policy.

CITP seeks Visiting Fellows and Postdoctoral Research Associates for the 2015-2016 year who work at the intersection of digital technology and public life, with backgrounds in fields including computer science, sociology, public policy, engineering, economics, law, and civil service. Visiting Fellow appointments are typically for nine months, commencing on September 1; postdoctoral appointments are for one to two years, normally commencing on July 1. Applicants may be appointed as a Visiting Fellow, Visiting Researcher, or Postdoctoral Research Associate.

CITP also seeks candidates for our Microsoft Visiting Professor of Information and Technology Policy position. Applicants must be currently appointed faculty members at an academic institution and must be on leave from such an appointment during their time at CITP. The successful applicant is expected to be appointed to a term between ten months and two years old based on their individual circumstances.

For full consideration, applications should be submitted by February 1, 2015 through jobs.princeton.edu.

Click for details on the Postdoctoral Research Associate application
Click for details on the Visiting Fellow application
Click for details on the Microsoft Visiting Professor of Information and Technology Policy application

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“Information Sharing” Should Include the Public

The FBI recently issued a warning to U.S. businesses about the possibility of foreign-based malware attacks. According to a Reuters story by Jim Finkle:

The five-page, confidential “flash” FBI warning issued to businesses late on Monday provided some technical details about the malicious software used in the attack. It provided advice on how to respond to the malware and asked businesses to contact the FBI if they identified similar malware.

The report said the malware overrides all data on hard drives of computers, including the master boot record, which prevents them from booting up.

“The overwriting of the data files will make it extremely difficult and costly, if not impossible, to recover the data using standard forensic methods,” the report said.

The document was sent to security staff at some U.S. companies in an email that asked them not to share the information.

The information found its way to the press, as one would expect of widely-shared information that is of public interest.

My question is this: Why didn’t they inform the public?
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How do we decide how much to reveal? (Hint: Our privacy behavior might be socially constructed.)

[Let's welcome Aylin Caliskan-Islam, a graduate student at Drexel. In this post she discusses new work that applies machine learning and natural-language processing to questions of privacy and social behavior. — Arvind Narayanan.]

How do we decide how much to share online given that information can spread to millions in large social networks? Is it always our own decision or are we influenced by our friends? Let’s isolate this problem to one variable, private information. How much private information are we sharing in our posts and are we the only authority controlling how much private information to divulge in our textual messages? Understanding how privacy behavior is formed could give us key insights for choosing our privacy settings, friends circles, and how much privacy to sacrifice in social networks. Christakis and Fowler’s network analytics study showed that obesity spreads through social ties. In another study, they explain that smoking cessation is a collective behavior. Our intuition before analyzing end users’ privacy behavior was that privacy behavior might also be under the effect of network phenomena.

In a recent paper that appeared at the 2014 Workshop on Privacy in the Electronic Society, we present a novel method for quantifying privacy behavior of users by using machine learning classifiers and natural-language processing techniques including topic categorization, named entity recognition, and semantic classification. Following the intuition that some textual data is more private than others, we had Amazon Mechanical Turk workers label tweets of hundreds of users as private or not based on nine privacy categories that were influenced by Wang et al.’s Facebook regrets categories and Sleeper et al.’s Twitter regrets categories. These labels were used to associate a privacy score with each user to reflect the amount of private information they reveal. We trained a machine learning classifier based on the calculated privacy scores to predict the privacy scores of 2,000 Twitter users whose data were collected through the Twitter API.
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