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XRay: Enhancing the Web’s Transparency with Differential Correlation

Tuesday, July 29, 2014 - 3:45pm
Authors: 

Mathias Lécuyer, Guillaume Ducoffe, Francis Lan, Andrei Papancea, Theofilos Petsios, Riley Spahn, Augustin Chaintreau, and Roxana Geambasu, Columbia University

Abstract: 

Today’s Web services – such as Google, Amazon, and Facebook – leverage user data for varied purposes, including personalizing recommendations, targeting advertisements, and adjusting prices. At present, users have little insight into how their data is being used. Hence, they cannot make informed choices about the services they choose.

To increase transparency, we developed XRay, the first fine-grained, robust, and scalable personal data tracking system for the Web. XRay predicts which data in an arbitrary Web account (such as emails, searches, or viewed products) is being used to target which outputs (such as ads, recommended products, or prices). XRay’s core functions are service agnostic and easy to instantiate for new services, and they can track data within and across services. To make predictions independent of the audited service, XRay relies on the following insight: by comparing outputs from different accounts with similar, but not identical, subsets of data, one can pinpoint targeting through correlation. We show both theoretically, and through experiments on Gmail, Amazon, and YouTube, that XRay achieves high precision and recall by correlating data from a surprisingly small number of extra accounts.

Mathias Lécuyer, Columbia University

Guillaume Ducoffe, Columbia University

Francis Lan, Columbia University

Andrei Papancea, Columbia University

Theofilos Petsios, Columbia University

Riley Spahn, Columbia University

Augustin Chaintreau, Columbia University

Roxana Geambasu, Columbia University

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BibTeX
@inproceedings {184393,
author = {Mathias L{\'e}cuyer and Guillaume Ducoffe and Francis Lan and Andrei Papancea and Theofilos Petsios and Riley Spahn and Augustin Chaintreau and Roxana Geambasu},
title = {XRay: Enhancing the Web{\textquoteright}s Transparency with Differential Correlation},
booktitle = {23rd {USENIX} Security Symposium ({USENIX} Security 14)},
year = {2014},
isbn = {978-1-931971-15-7},
address = {San Diego, CA},
pages = {49--64},
url = {https://www.usenix.org/conference/usenixsecurity14/technical-sessions/presentation/lecuyer},
publisher = {{USENIX} Association},
month = aug,
}
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