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With increasing popularity of Machine Learning as a Service (MLaaS), ML models trained from public and proprietary data are deployed in the cloud and deliver prediction services to users. However, as the prediction API becomes a new attack surface, growing concerns have arisen on the confidentiality of ML models. Existing literatures show their vulnerability under model extraction (ME) attacks, while their private training data is vulnerable to another type of attacks, namely, membership inference (MI). In this paper, we show that ME and MI can reinforce each other through a chained and iterative reaction, which can significantly boost ME attack accuracy and improve MI by saving the query cost. As such, we build a framework MExMI for pool-based active model extraction (PAME) to exploit MI through three modules: “MI Pre-Filter”, “MI Post-Filter”, and “semi-supervised boosting”. Experimental results show that MExMI can improve up to 11.14% from the best known PAME attack and reach 94.07% fidelity with only 16k queries. Furthermore, the precision and recall of the MI attack in MExMI are on par with state-of-the-art MI attack which needs 150k queries.
Author Information
Yaxin Xiao (The Hong Kong Polytechnic University)
Qingqing Ye (Hong Kong Polytechnic University)
Haibo Hu (Hong Kong Polytechnic University)
Huadi Zheng
Chengfang Fang (Huawei International)
Chengfang Fang obtained his Ph.D. degree from National University of Singapore before joining Huawei. He has been working on security and privacy protection in several areas including machine learning, internet of things, mobile device and biometrics for more than 10 years. He has published over 20 research papers and obtained 15 patents in the domain. He is currently a principle researcher in Huawei Singapore Research Center.
Jie Shi (Huawei International.)
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