We study the role of information complexity in privacy leakage about an attribute of an adversary's interest, which is not known a priori to the system designer.
In this paper, we propose a framework for privacy-preserving approximate near neighbor search via stochastic sparsifying encoding.
We propose a practical framework to address the problem of privacy-aware image sharing in large-scale setups.
We investigate the privacy of two approaches to (biometric) template protection: Helper Data Systems and Sparse Ternary Coding with Ambiguization.
In this paper, we address the problem of data reconstruction from privacy-protected templates, based on recent concept of sparse ternary coding with ambiguization (STCA).
We introduce a clustering principle that is based on evaluation of a parametric min-max measure for the discriminative prior.
The sparsifying transform and privacy amplification are not symmetric for the data owner and data user.
Peer-to-Peer (P2P) networks as distributed solutions are used in a variety of applications.