Towards Privacy-Preserving, Real-Time and Lossless Feature Matching
This work addresses privacy risks in visual retrieval systems for users and applications, representing an incremental improvement over existing methods.
The paper tackles the problem of privacy leakage in visual retrieval applications by proposing SecureVector, a plug-in module that achieves real-time and lossless feature matching with higher security levels than current state-of-the-art methods, as demonstrated in experiments on face recognition, person re-identification, and image retrieval.
Most visual retrieval applications store feature vectors for downstream matching tasks. These vectors, from where user information can be spied out, will cause privacy leakage if not carefully protected. To mitigate privacy risks, current works primarily utilize non-invertible transformations or fully cryptographic algorithms. However, transformation-based methods usually fail to achieve satisfying matching performances while cryptosystems suffer from heavy computational overheads. In addition, secure levels of current methods should be improved to confront potential adversary attacks. To address these issues, this paper proposes a plug-in module called SecureVector that protects features by random permutations, 4L-DEC converting and existing homomorphic encryption techniques. For the first time, SecureVector achieves real-time and lossless feature matching among sanitized features, along with much higher security levels than current state-of-the-arts. Extensive experiments on face recognition, person re-identification, image retrieval, and privacy analyses demonstrate the effectiveness of our method. Given limited public projects in this field, codes of our method and implemented baselines are made open-source in https://github.com/IrvingMeng/SecureVector.