Search Results for author:
Found 12 papers, 3 papers with code
Deep Privacy Funnel Model: From a Discriminative to a Generative Approach with an Application to Face Recognition
In this study, we apply the information-theoretic Privacy Funnel (PF) model to the domain of face recognition, developing a novel method for privacy-preserving representation learning within an end-to-end training framework.
Deep Variational Privacy Funnel: General Modeling with Applications in Face Recognition
In this study, we harness the information-theoretic Privacy Funnel (PF) model to develop a method for privacy-preserving representation learning using an end-to-end training framework.
Bottlenecks CLUB: Unifying Information-Theoretic Trade-offs Among Complexity, Leakage, and Utility
In this work, we propose a general family of optimization problems, termed as complexity-leakage-utility bottleneck (CLUB) model, which (i) provides a unified theoretical framework that generalizes most of the state-of-the-art literature for the information-theoretic privacy models, (ii) establishes a new interpretation of the popular generative and discriminative models, (iii) constructs new insights to the generative compression models, and (iv) can be used in the fair generative models.
Variational Leakage: The Role of Information Complexity in Privacy Leakage
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.
Privacy-Preserving Near Neighbor Search via Sparse Coding with Ambiguation
In this paper, we propose a framework for privacy-preserving approximate near neighbor search via stochastic sparsifying encoding.
Privacy-Preserving Image Sharing via Sparsifying Layers on Convolutional Groups
We propose a practical framework to address the problem of privacy-aware image sharing in large-scale setups.
Single-Component Privacy Guarantees in Helper Data Systems and Sparse Coding with Ambiguation
We investigate the privacy of two approaches to (biometric) template protection: Helper Data Systems and Sparse Ternary Coding with Ambiguization.
Reconstruction of Privacy-Sensitive Data from Protected Templates
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).
Clustering with Jointly Learned Nonlinear Transforms Over Discriminating Min-Max Similarity/Dissimilarity Assignment
We introduce a clustering principle that is based on evaluation of a parametric min-max measure for the discriminative prior.
Network Learning with Local Propagation
This paper presents a locally decoupled network parameter learning with local propagation.
Privacy Preserving Identification Using Sparse Approximation with Ambiguization
The sparsifying transform and privacy amplification are not symmetric for the data owner and data user.
BSROne: Binary Search with Routing of O(1); A Scalable Circular Design for Distributed Networks
Peer-to-Peer (P2P) networks as distributed solutions are used in a variety of applications.
