Deep Private-Feature Extraction

TitleDeep Private-Feature Extraction
Publication TypeJournal Article
Year of Publication2020
AuthorsOsia, S. A., A. Taheri, A. S. Shamsabadi, K. Katevas, H. Haddadi, and H. R. Rabiee
JournalIEEE Transactions on Knowledge and Data Engineering
Start Page54
Date Published01/2020
Accession Number 19291972
KeywordsDeep Learning, feature extraction, Information Theory, Privacy
AbstractWe 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 trade-off. We then implement and evaluate the performance of DPFEon smartphones to understand its complexity, resource demands, and efficiency trade-offs. 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 information
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