978 citations · 1.2k across the 16 of their papers we have counts for
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QueryCheetah: Fast Automated Discovery of Attribute Inference Attacks Against Query-Based Systems
Bozhidar Stevanoski, Ana-Maria Cretu, Yves-Alexandre de Montjoye
Query-based systems (QBSs) are one of the key approaches for sharing data. QBSs allow analysts to request aggregate information from a private protected dataset. Attacks are a cruc…
A Zero Auxiliary Knowledge Membership Inference Attack on Aggregate Location Data
Vincent Guan, Florent Guépin, Ana-Maria Cretu +1
Location data is frequently collected from populations and shared in aggregate form to guide policy and decision making. However, the prevalence of aggregated data also raises the…
Certification for Differentially Private Prediction in Gradient-Based Training
Matthew Wicker, Philip Sosnin, Igor Shilov +5
We study private prediction where differential privacy is achieved by adding noise to the outputs of a non-private model. Existing methods rely on noise proportional to the global…
The Mosaic Memory of Large Language Models
Igor Shilov, Matthieu Meeus, Yves-Alexandre de Montjoye
As Large Language Models (LLMs) become widely adopted, understanding how they learn from, and memorize, training data becomes crucial. Memorization in LLMs is widely assumed to onl…
Lost in the Averages: A New Specific Setup to Evaluate Membership Inference Attacks Against Machine Learning Models
Nataša Krčo, Florent Guépin, Matthieu Meeus +2
Synthetic data generators and machine learning models can memorize their training data, posing privacy concerns. Membership inference attacks (MIAs) are a standard method of estima…
Re-pseudonymization Strategies for Smart Meter Data Are Not Robust to Deep Learning Profiling Attacks
Ana-Maria Cretu, Miruna Rusu, Yves-Alexandre de Montjoye
Smart meters, devices measuring the electricity and gas consumption of a household, are currently being deployed at a fast rate throughout the world. The data they collect are extr…