August 18, 2018

Making Excuses for Fees on Electronic Public Records

Schultze Hogan LetterI wrote a letter to Judge Hogan, the recently appointed Director of the Administrative Office of the US Courts. I wanted to make the case directly to him that the courts should do the right thing — and that what they are doing right now is against the law. I was assured by his colleagues on the bench that Hogan is a reasonable and judicious person, and that he would at least hear me out. Yesterday, his administrative assistant replied to me. She said that he had forwarded the letter to the people in the Public Access and Records Management Division (PARMD), and that he didn’t want to talk to me. She said that I could contact Public Affairs Office if I wanted to discuss it further. The PARMD folks have, in the past, forwarded my requests for things like the congressionally mandated Judiciary Information Technology Fund Report to the Public Affairs folks, who of course never respond.

So, rather than participating in yet another bureaucratic run-around, I thought I’d outline the series of poor excuses that the Administrative Office has offered to justify their fees. If you’re a lawyer reading this, I invite you to consider what a lawsuit might look like. My email address is

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Anticensorship in the Internet's Infrastructure

I’m pleased to announce a research result that Eric Wustrow, Scott Wolchok, Ian Goldberg, and I have been working on for the past 18 months: Telex, a new approach to circumventing state-level Internet censorship. Telex is markedly different from past anticensorship efforts, and we believe it has the potential to shift the balance of power in the censorship arms race.

What makes Telex different from previous approaches:

  • Telex operates in the network infrastructure — at any ISP between the censor’s network and non-blocked portions of the Internet — rather than at network end points. This approach, which we call “end-to-middle” proxying, can make the system robust against countermeasures (such as blocking) by the censor.
  • Telex focuses on avoiding detection by the censor. That is, it allows a user to circumvent a censor without alerting the censor to the act of circumvention. It complements anonymizing services like Tor (which focus on hiding with whom the user is attempting to communicate instead of that that the user is attempting to have an anonymous conversation) rather than replacing them.
  • Telex employs a form of deep-packet inspection — a technology sometimes used to censor communication — and repurposes it to circumvent censorship.
  • Other systems require distributing secrets, such as encryption keys or IP addresses, to individual users. If the censor discovers these secrets, it can block the system. With Telex, there are no secrets that need to be communicated to users in advance, only the publicly available client software.
  • Telex can provide a state-level response to state-level censorship. We envision that friendly countries would create incentives for ISPs to deploy Telex.

For more information, keep reading, or visit the Telex website.

The Problem

Government Internet censors generally use firewalls in their network to block traffic bound for certain destinations, or containing particular content. For Telex, we assume that the censor government desires generally to allow Internet access (for economic or political reasons) while still preventing access to specifically blacklisted content and sites. That means Telex doesn’t help in cases where a government pulls the plug on the Internet entirely. We further assume that the censor allows access to at least some secure HTTPS websites. This is a safe assumption, since blocking all HTTPS traffic would cut off practically every site that uses password logins.

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Many anticensorship systems work by making an encrypted connection (called a “tunnel”) from the user’s computer to a trusted proxy server located outside the censor’s network. This server relays requests to censored websites and returns the responses to the user over the encrypted tunnel. This approach leads to a cat-and-mouse game, where the censor attempts to discover and block the proxy servers. Users need to learn the address and login information for a proxy server somehow, and it’s very difficult to broadcast this information to a large number of users without the censor also learning it.

How Telex Works

Telex turns this approach on its head to create what is essentially a proxy server without an IP address. In fact, users don’t need to know any secrets to connect. The user installs a Telex client app (perhaps by downloading it from an intermittently available website or by making a copy from a friend). When the user wants to visit a blacklisted site, the client establishes an encrypted HTTPS connection to a non-blacklisted web server outside the censor’s network, which could be a normal site that the user regularly visits. Since the connection looks normal, the censor allows it, but this connection is only a decoy.

The client secretly marks the connection as a Telex request by inserting a cryptographic tag into the headers. We construct this tag using a mechanism called public-key steganography. This means anyone can tag a connection using only publicly available information, but only the Telex service (using a private key) can recognize that a connection has been tagged.

As the connection travels over the Internet en route to the non-blacklisted site, it passes through routers at various ISPs in the core of the network. We envision that some of these ISPs would deploy equipment we call Telex stations. These devices hold a private key that lets them recognize tagged connections from Telex clients and decrypt these HTTPS connections. The stations then divert the connections to anti­censorship services, such as proxy servers or Tor entry points, which clients can use to access blocked sites. This creates an encrypted tunnel between the Telex user and Telex station at the ISP, redirecting connections to any site on the Internet.

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Telex doesn’t require active participation from the censored websites, or from the non-censored sites that serve as the apparent connection destinations. However, it does rely on ISPs to deploy Telex stations on network paths between the censor’s network and many popular Internet destinations. Widespread ISP deployment might require incentives from governments.

Development so Far

At this point, Telex is a concept rather than a production system. It’s far from ready for real users, but we have developed proof-of-concept software for researchers to experiment with. So far, there’s only one Telex station, on a mock ISP that we’re operating in our lab. Nevertheless, we have been using Telex for our daily web browsing for the past four months, and we’re pleased with the performance and stability. We’ve even tested it using a client in Beijing and streamed HD YouTube videos, in spite of YouTube being censored there.

Telex illustrates how it is possible to shift the balance of power in the censorship arms race, by thinking big about the problem. We hope our work will inspire discussion and further research about the future of anticensorship technology.

You can find more information and prototype software at the Telex website, or read our technical paper, which will appear at Usenix Security 2011 in August.

"You Might Also Like:" Privacy Risks of Collaborative Filtering

Ann Kilzer, Arvind Narayanan, Ed Felten, Vitaly Shmatikov, and I have released a new research paper detailing the privacy risks posed by collaborative filtering recommender systems. To examine the risk, we use public data available from Hunch, LibraryThing, Last.fm, and Amazon in addition to evaluating a synthetic system using data from the Netflix Prize dataset. The results demonstrate that temporal changes in recommendations can reveal purchases or other transactions of individual users.

To help users find items of interest, sites routinely recommend items similar to a given item. For example, product pages on Amazon contain a “Customers Who Bought This Item Also Bought” list. These recommendations are typically public, and they are the product of patterns learned from all users of the system. If customers often purchase both item A and item B, a collaborative filtering system will judge them to be highly similar. Most sites generate ordered lists of similar items for any given item, but some also provide numeric similarity scores.

Although item similarity is only indirectly related to individual transactions, we determined that temporal changes in item similarity lists or scores can reveal details of those transactions. If you’re a Mozart fan and you listen to a Justin Bieber song, this choice increases the perceived similarity between Justin Bieber and Mozart. Because similarity lists and scores are based on perceived similarity, your action may result in changes to these scores or lists.

Suppose that an attacker knows some of your past purchases on a site: for example, past item reviews, social networking profiles, or real-world interactions are a rich source of information. New purchases will affect the perceived similarity between the new items and your past purchases, possibility causing visible changes to the recommendations provided for your previously purchased items. We demonstrate that an attacker can leverage these observable changes to infer your purchases. Among other things, these attacks are complicated by the fact that multiple users simultaneously interact with a system and updates are not immediate following a transaction.

To evaluate our attacks, we use data from Hunch, LibraryThing, Last.fm, and Amazon. Our goal is not to claim privacy flaws in these specific sites (in fact, we often use data voluntarily disclosed by their users to verify our inferences), but to demonstrate the general feasibility of inferring individual transactions from the outputs of collaborative filtering systems. Among their many differences, these sites vary dramatically in the information that they reveal. For example, Hunch reveals raw item-to-item correlation scores, but Amazon reveals only lists of similar items. In addition, we examine a simulated system created using the Netflix Prize dataset. Our paper outlines the experimental results.

While inference of a Justin Bieber interest may be innocuous, inferences could expose anything from dissatisfaction with a job to health issues. Our attacks assume that a victim reveals certain past transactions, but users may publicly reveal certain transactions while preferring to keep others private. Ultimately, users are best equipped to determine which transactions would be embarrassing or otherwise problematic. We demonstrate that the public outputs of recommender systems can reveal transactions without user knowledge or consent.

Unfortunately, existing privacy technologies appear inadequate here, failing to simultaneously guarantee acceptable recommendation quality and user privacy. Mitigation strategies are a rich area for future work, and we hope to work towards solutions with others in the community.

Worth noting is that this work suggests a risk posed by any feature that adapts in response to potentially sensitive user actions. Unless sites explicitly consider the data exposed, such features may inadvertently leak details of these underlying actions.

Our paper contains additional details. This work was presented earlier today at the 2011 IEEE Symposium on Security and Privacy. Arvind has also blogged about this work.