Differential Privacy

Merlin, Morgan, and the Importance of Thresholds and Priors

Membership inference attacks are effective even for skewed priors.

Revisiting Membership Inference Under Realistic Assumptions

We propose novel membership inference attack and a threshold selection procedure to improve the existing attacks.

Efficient Privacy-Preserving Nonconvex Optimization

We propose differentially private algorithm for non-convex empirical risk minimization with reduced gradient complexity.

Evaluating Differentially Private Machine Learning in Practice

We compare the privacy leakage of ML models trained with different differential privacy relaxations and different privacy budgets.

Distributed Learning without Distress: Privacy-Preserving Empirical Risk Minimization

We combine differential privacy and MPC for privacy preserving distributed learing of strongly-convex ERM algorithms.

Evaluating Differentially Private Machine Learning in Practice

What seems safe, might not be safe in practice.

Analysis of Private ML Models

Comparing the differential privacy implementations by quantifying their privacy leakage.

Privacy Preserving Machine Learning

Combining differential privacy and multi-party computation techniques for private machine learning.