Deep Private-Feature Extraction

Seyed Ali Osia; Ali Taheri; Ali Shahin Shamsabadi; Kleomenis Katevas; Hamed Haddadi; Hamid R. Rabiee

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arXiv · 1802.03151

Deep Private-Feature Extraction

Seyed Ali Osia·Ali Taheri·Ali Shahin Shamsabadi·Kleomenis Katevas·Hamed Haddadi·Hamid R. Rabiee

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Abstract

We present and evaluate Deep Private-Feature Extractor (DPFE), a deep model which is trained and evaluated based on information theoretic constraints. Using the selective exchange of information between a user's device and a service provider, DPFE enables the user to prevent certain sensitive information from being shared with a service provider, while allowing them to extract approved information using their model. We introduce and utilize the log-rank privacy, a novel measure to assess the effectiveness of DPFE in removing sensitive information and compare different models based on their accuracy-privacy tradeoff. We then implement and evaluate the performance of DPFE on smartphones to understand its complexity, resource demands, and efficiency tradeoffs. Our results on benchmark image datasets demonstrate that under moderate resource utilization, DPFE can achieve high accuracy for primary tasks while preserving the privacy of sensitive features.

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stat.ML cs.CR cs.CV cs.IT cs.LG math.IT

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BibTeX RIS

Seyed Ali Osia, Ali Taheri, Ali Shahin Shamsabadi, Kleomenis Katevas, Hamed Haddadi, Hamid R. Rabiee. 2018-02-09. Deep Private-Feature Extraction. https://arxiv.org/abs/1802.03151

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