| Paper ID | IFS-6.1 |
| Paper Title |
PRIVACY-PRESERVING NEAR NEIGHBOR SEARCH VIA SPARSE CODING WITH AMBIGUATION |
| Authors |
Behrooz Razeghi, University of Geneva, Switzerland; Sohrab Ferdowsi, HES-SO Geneva, Switzerland; Dimche Kostadinov, University of Zurich, Switzerland; Flavio P. Clamon, Harvard University, United States; Slava Voloshynovskiy, University of Geneva, United States |
| Session | IFS-6: Anonymization, Security and Privacy |
| Location | Gather.Town |
| Session Time: | Thursday, 10 June, 15:30 - 16:15 |
| Presentation Time: | Thursday, 10 June, 15:30 - 16:15 |
| Presentation |
Poster
|
| Topic |
Information Forensics and Security: [MMH] Multimedia Content Hash |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
In this paper, we propose a framework for privacy-preserving approximate near neighbor search via stochastic sparsifying encoding. The core of the framework relies on sparse coding with ambiguation (SCA) mechanism that introduces the notion of inherent shared secrecy based on the support intersection of sparse codes. This approach is `fairness-aware', in the sense that any point in the neighborhood has an equiprobable chance to be chosen. Our approach can be applied to raw data, latent representation of autoencoders, and aggregated local descriptors. The proposed method is tested on both synthetic i.i.d data and real image databases. |
