Zitao Chen (University of British Columbia), Karthik Pattabiraman (University of British Columbia)

Machine learning (ML) models are vulnerable to membership inference attacks (MIAs), which determine whether a given input is used for training the target model. While there have been many efforts to mitigate MIAs, they often suffer from limited privacy protection, large accuracy drop, and/or requiring additional data that may be difficult to acquire.

This work proposes a defense technique, HAMP that can achieve both strong membership privacy and high accuracy, without requiring extra data. To mitigate MIAs in different forms, we observe that they can be unified as they all exploit the ML model’s overconfidence in predicting training samples through different proxies. This motivates our design to enforce less confident prediction by the model, hence forcing the model to behave similarly on the training and testing samples. HAMP consists of a novel training framework with high-entropy soft labels and an entropy-based regularizer to constrain the model’s prediction while still achieving high accuracy. To further reduce privacy risk, HAMP uniformly modifies all the prediction outputs to become low-confidence outputs while preserving the accuracy, which effectively obscures the differences between the prediction on members and non-members.

We conduct extensive evaluation on five benchmark datasets, and show that HAMP provides consistently high accuracy and strong membership privacy. Our comparison with seven state-of- the-art defenses shows that HAMP achieves a superior privacy- utility trade off than those techniques.

View More Papers

Understanding the Implementation and Security Implications of Protective DNS...

Mingxuan Liu (Zhongguancun Laboratory; Tsinghua University), Yiming Zhang (Tsinghua University), Xiang Li (Tsinghua University), Chaoyi Lu (Tsinghua University), Baojun Liu (Tsinghua University), Haixin Duan (Tsinghua University; Zhongguancun Laboratory), Xiaofeng Zheng (Institute for Network Sciences and Cyberspace, Tsinghua University; QiAnXin Technology Research Institute & Legendsec Information Technology (Beijing) Inc.)

Read More

TALISMAN: Tamper Analysis for Reference Monitors

Frank Capobianco (The Pennsylvania State University), Quan Zhou (The Pennsylvania State University), Aditya Basu (The Pennsylvania State University), Trent Jaeger (The Pennsylvania State University, University of California, Riverside), Danfeng Zhang (The Pennsylvania State University, Duke University)

Read More

Don't Interrupt Me – A Large-Scale Study of On-Device...

Marian Harbach (Google), Igor Bilogrevic (Google), Enrico Bacis (Google), Serena Chen (Google), Ravjit Uppal (Google), Andy Paicu (Google), Elias Klim (Google), Meggyn Watkins (Google), Balazs Engedy (Google)

Read More

SOC Service Areas: Identification, Prioritization, and Implementation

Christopher Rodman, Breanna Kraus, Justin Novak (SEI/CERT)

Read More