Carmela González Troncoso

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Synthetic data has been advertised as a silver-bullet solution to privacy-preserving data publishing that addresses the shortcomings of traditional anonymisation techniques. The promise is that synthetic data drawn from generative models preserves the statistical properties of the original dataset but, at the same time, provides perfect protection against privacy attacks. In this work, we present the first quantitative evaluation of the privacy gain of synthetic data publishing and compare it to that of previous anonymisation techniques. Our evaluation of a wide range of state-of-the-art generative models demonstrates that synthetic data either does not prevent inference attacks or does not retain data utility. In other words, we empirically show that synthetic data does not provide a better tradeoff between privacy and utility than traditional anonymisation techniques. Furthermore, in contrast to traditional anonymisation, the privacy-utility tradeoff of synthetic data publishing is hard to predict. Because it is impossible to predict what signals a synthetic dataset will preserve and what information will be lost, synthetic data leads to a highly variable privacy gain and unpredictable utility loss. In summary, we find that synthetic data is far from the holy grail of privacy-preserving data publishing.

USENIX ASSOC2022

WEBGRAPH: Capturing Advertising and Tracking Information Flows for Robust Blocking

Carmela González Troncoso, Sandra Deepthy Siby

Users rely on ad and tracker blocking tools to protect their privacy. Unfortunately, existing ad and tracker blocking tools are susceptible to mutable advertising and tracking content. In this paper, we first demonstrate that a state-of-the-art ad and tracker blocker, ADGRAPH, is susceptible to such adversarial evasion techniques that are currently deployed on the web. Second, we introduce WEBGRAPH, the first ML-based ad and tracker blocker that detects ads and trackers based on their action rather than their content. By featurizing the actions that are fundamental to advertising and tracking information flows - e.g., storing an identifier in the browser or sharing an identifier with another tracker - WEB GRAPH performs nearly as well as prior approaches, but is significantly more robust to adversarial evasions. In particular, we show that WEBGRAPH achieves comparable accuracy to ADGRAPH, while significantly decreasing the success rate of an adversary from near-perfect for ADGRAPH to around 8% for WEBGRAPH. Finally, we show that WEB GRAPH remains robust to sophisticated adversaries that use adversarial evasion techniques beyond those currently deployed on the web.

USENIX ASSOC2022

Why the search for a privacy-preserving data sharing mechanism is failing

Carmela González Troncoso, Theresa Stadler

The rapidly growing demand to share data more openly creates a need for secure and privacy-preserving sharing technologies. However, there are multiple challenges associated with the development of a universal privacy-preserving data sharing mechanism, and existing solutions still fall short of their promises.

SPRINGERNATURE2022