June 15, 2019

"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.

Ninth Circuit Ruling in MDY v. Blizzard

The Ninth Circuit has ruled on the MDY v. Blizzard case, which involves contract, copyright, and DMCA claims. As with the district court ruling, I’ll withhold comment due to my involvement as an expert in the case, but the decision may be of interest to FTT readers.

[Editor: The EFF has initial reactions here. Techdirt also has an overview.]

Best Practices for Government Datasets: Wrap-Up

[This is the fifth and final post in a series on best practices for government datasets by Harlan Yu and me. (previous posts: 1, 2, 3, 4)]

For our final post in this series, we’ll discuss several issues not touched on by earlier posts, including data signing and the use of certain non-text file formats. The relatively brief discussions of these topics should not be interpreted as an indicator of their importance. The topics simply did not fit cleanly into earlier posts.

One significant omission from earlier posts is the issue of data signing with digital signatures. Before discussing this issue, let’s briefly discuss what a digital signature is. Suppose that you want to email me an IOU for $100. Later, I may want to prove that the IOU came from you—it’s of little value if you can claim that I made it up. Conversely, you may want the ability to prove whether the document has been altered. Otherwise, I could claim that you owe me $100,000.

Digital signatures help in proving the origin and authenticity of data. These signatures require that you create two related big numbers, known as keys: a private signing key (known only by you) and a public verification key. To generate a digital signature, you plug the data and your signing key into a complicated formula. The formula spits out another big number known a digital signature. Given the signature and your data, I can use the verification key to prove that the data came unmodified from you. Similarly, nobody can credibly sign modified data without your signing key—so you should be very careful to keep this key a secret.

Developers may want to ensure the authenticity of government data and to prove that authenticity to users. At first glance, the solution seems to be a simple application of digital signatures: agencies sign their data, and anyone can use the signatures to authenticate an agency’s data. In spite of their initially steep learning curve, tools like GnuPG provide straightforward file signing. In practice, the situation is more complicated. First, an agency must decide what data to sign. Perhaps a dataset contains numerous documents. Developers and other users may want signatures not only for the full dataset but also for individual documents in it.

Once an agency knows what to sign, it must decide who will perform the signing. Ideally, the employee producing the dataset would sign it immediately. Unfortunately, this solution requires all such employees to understand the signature tools and to know the agency’s signing key. Widespread distribution of the signing key increases the risk that it will be accidentally revealed. Therefore, a central party is likely to sign most data. Once data is signed, an agency must have a secure channel for delivering the verification key to consumers of the data—users cannot confirm the authenticity of signed data without this key. While signing a given file with a given key may not be hard, surrounding issues are more tricky. We offer no simple solution here, but further discussion of this topic between government agencies, developers, and the public could be useful for all parties.

Another issue that earlier posts did not address is the use of non-text spreadsheet formats, including Microsoft Excel’s XLS format. These formats can sometimes be useful because they allow the embedding of formulas and other rich information along with the data. Unfortunately, these formats are far more complex than raw text formats, so they present a greater challenge for automated processing tools. A comma-separated value (CSV) file is a straightforward text format that contains values separated by line breaks and commas. It provides an alternative to complicated spreadsheet formats. For example, the medal count from the 2010 Winter Olympics in CSV would be:

  Country,Gold,Silver,Bronze,Total
  USA,9,15,13,37
  Germany,10,13,7,30
  Canada,14,7,5,26
  Norway,9,8,6,23
  ...

Fortunately, the release of data in one format does not preclude its release in another format. Most spreadsheet programs provide an option to save data in CSV form. Agencies should release spreadsheet data in a textual format like CSV by default, but an agency should feel free to also release the data in XLS or other formats.

Similarly, agencies will sometimes release large files or groups of files in a compressed or bundled format (for example, ZIP, TAR, GZ, BZ). In these cases, agencies should prominently specify where users can freely obtain software and instructions for extracting the data. Because so many means of compressing and bundling files exist, agencies should not presume that the necessary tools and steps are obvious from the data files themselves.

The rules suggested throughout this series should be seen as best practices rather than hard-and-fast rules. We are still in the process of fleshing out several of these ideas ourselves, and exceptional cases sometimes justify exceptional treatment. In unusual cases, an agency may need to deviate from traditional best practices, but it should carefully consider (and perhaps document) its rationale for doing so. Rules are made to be broken, but they should not be broken for mere expedience.

Our hope is that this series will provide agencies with some points to consider prior to releasing data. Because of Data.gov and the increasing traction of openness and transparency initiatives, we expect to see many more datasets enter the public domain in the coming years. Some agencies will approach the release of bulk data with minimal previous experience. While this poses a challenge, it also present an opportunity for committed agencies to institute good practices early, before bad habits and poor-quality legacy datasets can accumulate. When releasing new datasets, agencies will make numerous conscious and unconscious choices that impact developers. We hope to help agencies understand developers’ challenges when making these choices.

After gathering input from the community, we plan to create a technical report based on this series of posts. Thanks to numerous readers for insightful feedback; your comments have influenced and clarified our thoughts. If any FTT readers inside or outside of government have additional comments about this post or others, please do pass them along.