| Title | Deep Private-Feature Extraction |
| Publication Type | Journal Article |
| Year of Publication | 2020 |
| Authors | Osia, S. A., A. Taheri, A. S. Shamsabadi, K. Katevas, H. Haddadi, and H. R. Rabiee |
| Journal | IEEE Transactions on Knowledge and Data Engineering |
| Volume | 32 |
| Issue | 1 |
| Start Page | 54 |
| Pagination | 54-66 |
| Date Published | 01/2020 |
| Accession Number | 19291972 |
| Keywords | Deep Learning, feature extraction, Information Theory, Privacy |
| 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 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 |
| DOI | <a href="http://dx.doi.org/10.1109/TKDE.2018.2878698&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;quot;&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;gt;https://doi.org/10.1109/TKDE.2018.2878698&amp;amp;amp;amp;amp |
