CAPter: Controllable Data Privacy Enhancement for Deep Learning Inference Services
Shimao Xu, Xiaopeng Ke, Hao Wu · 2024
The deep learning’s outstanding performance and high overhead facilitate the popular intelligent web service, i.e., deep learning inference service (DLIS). However, DLISes require users to send their personal data to the remote server, which raises a severe risk of data privacy compromise. Some works have been proposed to mitigate this problem, but they all preset a determined data protection goal, e.g., finding a single sweet point that balances privacy and utility. The preset protecting goal neglects the protection needs of different users. In this paper, we propose, CAPter, a controllable data privacy enhancement solution for DLISes to meet users’ different protection needs in practical DLIS usage. It takes as input the user data and enhancement level and enhances the data in a controllable data information reduction way. CAPter works without any prior knowledge of the private attributes. Comprehensive experiments that CAPter can effectively prevent the secondary inference attack and reconstruction attack by sacrificing 0.01 to 0.02 in accuracy and outperforms the baseline.