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 Route Origin Validation (ROV) Deployment in the Real...

Lancheng Qin (Tsinghua University, BNRist), Li Chen (Zhongguancun Laboratory), Dan Li (Tsinghua University, Zhongguancun Laboratory), Honglin Ye (Tsinghua University), Yutian Wang (Tsinghua University)

Read More

LoRDMA: A New Low-Rate DoS Attack in RDMA Networks

Shicheng Wang (Tsinghua University), Menghao Zhang (Beihang University & Infrawaves), Yuying Du (Information Engineering University), Ziteng Chen (Southeast University), Zhiliang Wang (Tsinghua University & Zhongguancun Laboratory), Mingwei Xu (Tsinghua University & Zhongguancun Laboratory), Renjie Xie (Tsinghua University), Jiahai Yang (Tsinghua University & Zhongguancun Laboratory)

Read More

Invisible Reflections: Leveraging Infrared Laser Reflections to Target Traffic...

Takami Sato (University of California Irvine), Sri Hrushikesh Varma Bhupathiraju (University of Florida), Michael Clifford (Toyota InfoTech Labs), Takeshi Sugawara (The University of Electro-Communications), Qi Alfred Chen (University of California, Irvine), Sara Rampazzi (University of Florida)

Read More