"Arvind Narayanan"

Carriers ignore studies that show they suck at preventing SIM-swap attacks

Now that many online services rely on sending SMSes to your phone to authenticate your identify, thieves and stalkers have created a whole "SIM swap" industry where they defraud your phone company or bribe employees to help them steal your phone account so they can break into all your other accounts. Read the rest

How to recognize AI snake oil

Princeton computer scientist Arvind Narayanan (previously) has posted slides and notes from a recent MIT talk on "How to recognize AI snake oil" in which he divides AI applications into three (nonexhaustive) categories and rates how difficult they are, and thus whether you should believe vendors who claim that their machine learning models can perform as advertised. Read the rest

IoT Inspector: Princeton releases a tool to snoop on home IoT devices and figure out what they're doing

IoT Inspector is a new tool from Princeton's computer science department; it snoops on the traffic from home IoT devices and performs analysis to determine who they phone home to, whether they use encryption, and what kinds of data they may be leaking. Read the rest

You can unscramble the hashes of humanity's 5 billion email addresses in ten milliseconds for $0.0069

Marketing companies frequently "anonymize" their dossiers on internet users using hashes of their email addresses -- rather than the email addresses themselves -- as identifiers in databases that are stored indefinitely, traded, sold, and leaked. Read the rest

Attacks that unmask anonymous blockchain transactions can be used against everyone who ever relied on the defective technique

In An Empirical Analysis of Traceability in the Monero Blockchain, a group of eminent computer scientists analyze a longstanding privacy defect in the Monero cryptocurrency, and reveal a new, subtle flaw, both of which can be used to potentially reveal the details of transactions and identify their parties. Read the rest

An incredibly important paper on whether data can ever be "anonymized" and how we should handle release of large data-sets

Even the most stringent privacy rules have massive loopholes: they all allow for free distribution of "de-identified" or "anonymized" data that is deemed to be harmless because it has been subjected to some process. Read the rest

Web analytics companies offer "replay sessions" that let corporations watch every click and keystroke for individual users

The "replay sessions" captured by surveillance-oriented "analytics" companies like Fullstory allow their customers -- "Walgreens, Zocdoc, Shopify, CareerBuilder, SeatGeek, Wix.com, Digital Ocean, DonorsChoose.org, and more" -- to watch everything you do when you're on their webpages -- every move of the mouse, every keystroke (even keystrokes you delete before submitting), and more, all attached to your real name, stored indefinitely, and shared widely with many, many "partners." Read the rest

Blockers will win the ad-blocking arms race

Ad-blockers begat ad-blocker-blockers, which begat ad-blocker-blocker-blockers, with no end in sight. Read the rest

How surveillance capitalism tracks you without cookies

Princeton computer science researchers Steven Englehardt and Arvind Narayanan (previously) have just published a new paper, Online tracking: A 1-million-site measurement and analysis, which documents the state of online tracking beyond mere cookies -- sneaky and often illegal techniques used to "fingerprint" your browsers and devices as you move from site to site, tracking you even when you explicitly demand not to be track and take countermeasures to prevent this. Read the rest

Web companies can track you -- and price-gouge you -- based on your battery life

In Online tracking: A 1-million-site measurement and analysis, eminent Princeton security researchers Steven Englehardt and Arvind Narayanan document the use of device battery levels -- accessible both through mobile platform APIs and HTML5 calls -- to track and identify users who are blocking cookies and other methods of tracking. Read the rest

Free Bitcoin textbook from Princeton

The Princeton Bitcoin Book by Arvind Narayanan, Joseph Bonneau, Edward Felten, Andrew Miller and Steven Goldfeder is a free download -- it's over 300 pages and is intended for people "looking to truly understand how Bitcoin works at a technical level and have a basic familiarity with computer science and programming." Read the rest

Big Data should not be a faith-based initiative

Cory Doctorow summarizes the problem with the idea that sensitive personal information can be removed responsibly from big data: computer scientists are pretty sure that's impossible.

Eternal vigilance app for social networks: treating privacy vulnerabilities like other security risks

Social networking sites are Skinner boxes designed to train you to undervalue your privacy. Since all the compromising facts of your life add less than a dollar to the market-cap of the average social network, they all push to add more "sharing" by default, with the result that unless you devote your life to it, you're going to find your personal info shared ever-more-widely by G+, Facebook, Linkedin, and other "social" services.

Arvind Narayanan has proposed a solution to this problem: a two-part system through which privacy researchers publish a steady stream of updates about new privacy vulnerabilities introduced by the social networking companies (part one), and your computer sifts through these and presents you with a small subset of the alerts that pertain to you and your own network use. Read the rest

Scalable stylometry: can we de-anonymize the Internet by analyzing writing style?

One of the most interesting technical presentations I attended in 2012 was the talk on "adversarial stylometry" given by a Drexel College research team at the 28C3 conference in Berlin. "Stylometry" is the practice of trying to ascribe authorship to an anonymous text by analyzing its writing style; "adversarial stylometry" is the practice of resisting stylometric de-anonymization by using software to remove distinctive characteristics and voice from a text.

Stanford's Arvind Narayanan describes a paper he co-authored on stylometry that has been accepted for the IEEE Symposium on Security and Privacy 2012. In On the Feasibility of Internet-Scale Author Identification (PDF) Narayanan and co-authors show that they can use stylometry to improve the reliability of de-anonymizing blog posts drawn from a large and diverse data-set, using a method that scales well. However, the experimental set was not "adversarial" -- that is, the authors took no countermeasures to disguise their authorship. It would be interesting to see how the approach described in the paper performs against texts that are deliberately anonymized, with and without computer assistance. The summary cites another paper by someone who found that even unaided efforts to disguise one's style makes stylometric analysis much less effective.

We made several innovations that allowed us to achieve the accuracy levels that we did. First, contrary to some previous authors who hypothesized that only relatively straightforward “lazy” classifiers work for this type of problem, we were able to avoid various pitfalls and use more high-powered machinery. Second, we developed new techniques for confidence estimation, including a measure very similar to “eccentricity” used in the Netflix paper.

Read the rest

Yet another creepy research paper proving you have no privacy online

Research by Carnegie Mellon professor Latanya Sweeney and other experts shows that an alarming number of seemingly innocuous, neutral, or "common" data points, can potentially identify an individual online. "Privacy law, mainly clinging to a traditional intuitive notion of identifiability, has largely not kept up with the technical reality," says the EFF's Seth Schoen:

A recent paper by Paul Ohm, "Broken Promises of Privacy: Responding to the Surprising Failure of Anonymization", provides a thorough introduction and a useful perspective on this issue. Prof. Ohm's paper is important reading for anyone interested in personal privacy, because it shows how deanonymization results achieved by researchers like Latanya Sweeney and Arvind Narayanan seriously undermine traditional privacy assumptions. In particular, the binary distinction between "personally-identifiable information" and "non-personally-identifiable information" is increasingly difficult to sustain. Our intuition that certain information is "anonymous" is often wrong.

What information is "personally identifiable"? (EFF Deep Links) Read the rest

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