Teleportation: Defense against Stealing Attacks of Data-Driven Healthcare APIs

The increased popularity of digital healthcare services has prompted the development of different types of datadriven healthcare APIs on top of electronic health records, offering the convenience of aided diagnostic services without compromising privacy. Defense against the unauthorized extraction of healthcare APIs is important due to: 1) the unauthorized cloned model could serve online as fake healthcare service providers and pose harm to the general public; 2) protected training data containing private electronic health records could be further extracted from the stolen model. It is therefore important to protect the data-driven healthcare APIs from unauthorized clone and extraction. In this work, we propose a principled defense strategy with adaptive teleportation of incoming queries to effectively guard against extraction attacks of healthcare APIs. The proposed mechanism prevents unauthorized copy of model functionality while maintaining the utility of APIs to serve benign queries. The adaptive teleportation operations are generated based on the formulated bi-level optimization target and follows the evolution trajectory depicted by the Wasserstein gradient flows, which effectively push attacking queries to cross decision boundary while constraining the deviation level of benign queries, utilizing the fact that attacker generated pseudo-queries are mostly closer to decision boundaries than normal queries. This provides misleading information on malicious queries while preserving model utility. We performed detailed analysis of the proposed mechanism on three healthcare related prediction tasks including in-hospital mortality, bleed risk and ischemic risk prediction for validation of its effectiveness under different types of attacking scenarios. The proposed mechanism is significantly more effective to suppress the performance of cloned model while maintaining comparable serving utility compared to existing defense approaches.