2 citations · 2 across the 8 of their papers we have counts for
4 papers · 1 filter
The DCR Delusion: Measuring the Privacy Risk of Synthetic Data
Zexi Yao, Nataša Krčo, Georgi Ganev +1
Synthetic data has become an increasingly popular way to share data without revealing sensitive information. Though Membership Inference Attacks (MIAs) are widely considered the go…
Exploring the limits of strong membership inference attacks on large language models
Jamie Hayes, Ilia Shumailov, Christopher A. Choquette-Choo +13
State-of-the-art membership inference attacks (MIAs) typically require training many reference models, making it difficult to scale these attacks to large pre-trained language mode…
DeSIA: Attribute Inference Attacks Against Limited Fixed Aggregate Statistics
Yifeng Mao, Bozhidar Stevanoski, Yves-Alexandre de Montjoye
Empirical inference attacks are a popular approach for evaluating the privacy risk of data release mechanisms in practice. While an active attack literature exists to evaluate mach…
Sub-optimal Learning in Meta-Classifier Attacks: A Study of Membership Inference on Differentially Private Location Aggregates
Yuhan Liu, Florent Guepin, Igor Shilov +1
The widespread collection and sharing of location data, even in aggregated form, raises major privacy concerns. Previous studies used meta-classifier-based membership inference att…