InfoScissors: Defense against Data Leakage in Collaborative Inference through the Lens of Mutual Information
Keywords: Data leakage; Collaborative inference
Abstract: Edge-cloud collaborative inference empowers resource-limited IoT devices to support deep learning applications without disclosing their raw data to the cloud server, thus protecting user's data. Nevertheless, prior research has shown that collaborative inference still results in the exposure of input and predictions from edge devices. To defend against such data leakage in collaborative inference, we introduce InfoScissors, a defense strategy designed to reduce the mutual information between a model's intermediate outcomes and the device's input and predictions. We evaluate our defense on several datasets in the context of diverse attacks and offer a theoretical robustness guarantee.
Primary Area: representation learning for computer vision, audio, language, and other modalities
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Submission Number: 3916
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