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.

介绍了 DPFE，一种基于信息论约束的深度模型，通过选择性信息交换，可以保证用户的隐私，并在主要任务时保持高准确性。同时介绍了 DPFE 的隐私度量方法 log-rank privacy，并在智能手机上实现并评估了其性能。