Teleportation: Defense Against Stealing Attacks of Data-Driven Healthcare APIs
Summary
This research addresses the problem of stealing attacks against healthcare APIs (application programming interfaces, which are tools that let software systems communicate with each other), where attackers try to copy or extract data from medical AI models. The authors propose a defense strategy called "adaptive teleportation" that modifies incoming queries (requests) in clever ways to fool attackers while still allowing legitimate users to get accurate results from the healthcare API.
Solution / Mitigation
The source proposes 'adaptive teleportation of incoming queries' as the defense mechanism. According to the text, '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.' This approach 'provides misleading information on malicious queries while preserving model utility.' The authors validated this mechanism on three healthcare prediction tasks (inhospital mortality, bleed risk, and ischemic risk prediction) and found it 'significantly more effective to suppress the performance of cloned model while maintaining comparable serving utility compared to existing defense approaches.'
Classification
Related Issues
Original source: http://ieeexplore.ieee.org/document/11099051
First tracked: March 16, 2026 at 04:14 PM
Classified by LLM (prompt v3) · confidence: 85%