Deep Private-Feature Extraction
IEEE Transactions on Knowledge and Data Engineering(2020)
摘要
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 trade-off. We then implement and evaluate the performance of
DPFE
on 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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关键词
Data privacy,Feature extraction,Privacy,Data models,Task analysis,Training
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