Empirical defenses for machine learning privacy forgo the provable guarantees of
differential privacy in the hope of achieving higher utility while resisting realistic adversaries …

Membership inference attacks aim to detect if a particular data point was used in training a
model. We design a novel statistical test to perform robust membership inference attacks …

Using only 34 published tables, we reconstruct five variables (census block, sex, age, race,
and ethnicity) in the confidential 2010 Census person records. Using the 38-bin age variable …

Machine learning models sometimes memorize sensitive training data features, posing
privacy risks. To control such privacy risks, Dwork et al. proposed the definition of differential …

We study what provable privacy attacks can be shown on trained, 2-layer ReLU neural
networks. We explore two types of attacks; data reconstruction attacks, and membership …

Membership inference attacks aim to infer whether an individual record was used to train a
model, serving as a key tool for disclosure auditing. While such evaluations are useful to …

Given the complexity and lack of transparency in deep neural networks (DNNs), extensive
efforts have been made to make these systems more interpretable or explain their behaviors …

In this paper, we introduce WaKA (Wasserstein K-nearest neighbors Attribution), a novel
attribution method that leverages principles from the LiRA (Likelihood Ratio Attack) …

Modern automotive infotainment systems offer a complex and wide array of controls and
features through various interaction methods. However, such complexity can distract the …

A Data-Centric Analysis of Membership Inference Attacks Page 1 Master’s thesis Master’s
Programme in Data Science A Data-Centric Analysis of Membership Inference Attacks Hibiki Ito …