2021 IEEE International Conference on Acoustics, Speech and Signal Processing
6-11 June 2021 • Toronto, Ontario, Canada
Extracting Knowledge from Information
| Paper ID | IFS-6.5 | ||
| Paper Title | ACTIVE PRIVACY-UTILITY TRADE-OFF AGAINST A HYPOTHESIS TESTING ADVERSARY | ||
| Authors | Ecenaz Erdemir, Pier Luigi Dragotti, Deniz Gündüz, Imperial College London, United Kingdom | ||
| 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: [ADP] Anonymization And Data Privacy | ||
| IEEE Xplore Open Preview | Click here to view in IEEE Xplore | ||
| Abstract | We consider a user releasing her data containing some personal information in return of a service. We model user's personal information as two correlated random variables, one of them, called the secret variable, is to be kept private, while the other, called the useful variable, is to be disclosed for utility. We consider active sequential data release, where at each time step the user chooses from among a finite set of release mechanisms, each revealing some information about the user's personal information, i.e., the true hypotheses, albeit with different statistics. The user manages data release in an online fashion such that maximum amount of information is revealed about the latent useful variable, while the confidence for the sensitive variable is kept below a predefined level. For the utility, we consider both the probability of correct detection of the useful variable and the mutual information (MI) between the useful variable and released data. We formulate both problems as a Markov decision process (MDP), and numerically solve them by advantage actor-critic (A2C) deep reinforcement learning (RL). | ||