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Found 3 papers, 2 papers with code
MIAShield: Defending Membership Inference Attacks via Preemptive Exclusion of Members
In membership inference attacks (MIAs), an adversary observes the predictions of a model to determine whether a sample is part of the model's training data.
DP-UTIL: Comprehensive Utility Analysis of Differential Privacy in Machine Learning
In this paper, we present, DP-UTIL, a holistic utility analysis framework of DP across the ML pipeline with focus on input perturbation, objective perturbation, gradient perturbation, output perturbation, and prediction perturbation.
PRICURE: Privacy-Preserving Collaborative Inference in a Multi-Party Setting
This paper presents PRICURE, a system that combines complementary strengths of secure multi-party computation (SMPC) and differential privacy (DP) to enable privacy-preserving collaborative prediction among multiple model owners.
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