Hongsheng Hu (CSIRO's Data61), Shuo Wang (CSIRO's Data61), Jiamin Chang (University of New South Wales), Haonan Zhong (University of New South Wales), Ruoxi Sun (CSIRO's Data61), Shuang Hao (University of Texas at Dallas), Haojin Zhu (Shanghai Jiao Tong University), Minhui Xue (CSIRO's Data61)

The right to be forgotten requires the removal or "unlearning" of a user's data from machine learning models. However, in the context of Machine Learning as a Service (MLaaS), retraining a model from scratch to fulfill the unlearning request is impractical due to the lack of training data on the service provider's side (the server). Furthermore, approximate unlearning further embraces a complex trade-off between utility (model performance) and privacy (unlearning performance). In this paper, we try to explore the potential threats posed by unlearning services in MLaaS, specifically over-unlearning, where more information is unlearned than expected. We propose two strategies that leverage over-unlearning to measure the impact on the trade-off balancing, under black-box access settings, in which the existing machine unlearning attacks are not applicable. The effectiveness of these strategies is evaluated through extensive experiments on benchmark datasets, across various model architectures and representative unlearning approaches. Results indicate significant potential for both strategies to undermine model efficacy in unlearning scenarios. This study uncovers an underexplored gap between unlearning and contemporary MLaaS, highlighting the need for careful considerations in balancing data unlearning, model utility, and security.

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Liheng Chen (Institute of Information Engineering, Chinese Academy of Sciences; School of Cyber Security, University of Chinese Academy of Sciences; Institute for Network Science and Cyberspace of Tsinghua University), Zheming Li (Institute for Network Science and Cyberspace of Tsinghua University), Zheyu Ma (Institute for Network Science and Cyberspace of Tsinghua University), Yuan Li (Tsinghua University),…

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Symphony: Path Validation at Scale

Anxiao He (Zhejiang University), Jiandong Fu (Zhejiang University), Kai Bu (Zhejiang University), Ruiqi Zhou (Zhejiang University), Chenlu Miao (Zhejiang University), Kui Ren (Zhejiang University)

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Front-running Attack in Sharded Blockchains and Fair Cross-shard Consensus

Jianting Zhang (Purdue University), Wuhui Chen (Sun Yat-sen University), Sifu Luo (Sun Yat-sen University), Tiantian Gong (Purdue University), Zicong Hong (The Hong Kong Polytechnic University), Aniket Kate (Purdue University)

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ORL-AUDITOR: Dataset Auditing in Offline Deep Reinforcement Learning

Linkang Du (Zhejiang University), Min Chen (CISPA Helmholtz Center for Information Security), Mingyang Sun (Zhejiang University), Shouling Ji (Zhejiang University), Peng Cheng (Zhejiang University), Jiming Chen (Zhejiang University), Zhikun Zhang (CISPA Helmholtz Center for Information Security and Stanford University)

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