Masked Differential Privacy

Sina Sajadmanesh

Vikash Sehwag

Lingjuan Lyu

Vivek Sharma

David Schneider

Saquib Sarfraz

Rainer Stiefelhagen

ECCV-24

2024

Abstract

Privacy-preserving computer vision is an important emerg- ing problem in machine learning and artificial intelligence. The prevalent methods tackling this problem use differential privacy or anonymization and obfuscation techniques to protect the privacy of individuals. In both cases, the utility of the trained model is sacrificed heavily in this process. In this work, we propose an effective approach called masked differential privacy (MaskDP), which allows for controlling sensitive regions where differential privacy is applied, in contrast to applying DP on the en- tire input. Our method operates selectively on the data and allows for defining non-sensitive spatio-temporal regions without DP application or combining differential privacy with other privacy techniques within data samples. Experiments on four challenging action recognition datasets demonstrate that our proposed techniques result in better utility-privacy trade-offs compared to standard differentially private training in the es- pecially demanding ε < 1 regime.