Search Results for author:
Found 13 papers, 0 papers with code
Generalized Rainbow Differential Privacy
In this framework, datasets are nodes in a graph, and two neighboring datasets are connected by an edge.
Concatenated Classic and Neural (CCN) Codes: ConcatenatedAE
Small neural networks (NNs) used for error correction were shown to improve on classic channel codes and to address channel model changes.
Secure and Private Source Coding with Private Key and Decoder Side Information
The problem of secure source coding with multiple terminals is extended by considering a remote source whose noisy measurements are the correlated random variables used for secure source reconstruction.
Secure Joint Communication and Sensing
For independent and identically distributed states, perfect output feedback, and when part of the transmitted message should be kept secret, a partial characterization of the secrecy-distortion region is developed.
Rainbow Differential Privacy
We extend a previous framework for designing differentially private (DP) mechanisms via randomized graph colorings that was restricted to binary functions, corresponding to colorings in a graph, to multi-valued functions.
Function Computation Under Privacy, Secrecy, Distortion, and Communication Constraints
The problem of reliable function computation is extended by imposing privacy, secrecy, and storage constraints on a remote source whose noisy measurements are observed by multiple parties.
Quality of Service Guarantees for Physical Unclonable Functions
A public ring oscillator (RO) output dataset is used to illustrate that a truncated Gaussian distribution can be fitted to transformed RO outputs that are inputs to uniform scalar quantizers such that reliability guarantees can be provided for each bit extracted from any PUF device under additive Gaussian noise components by eliminating a small subset of PUF outputs.
Secure Multi-Function Computation with Private Remote Sources
We consider a distributed function computation problem in which parties observing noisy versions of a remote source facilitate the computation of a function of their observations at a fusion center through public communication.
Federated Learning with Local Differential Privacy: Trade-offs between Privacy, Utility, and Communication
We consider Gaussian mechanisms to preserve local differential privacy (LDP) of user data in the FL model with SGD.
On Skew Convolutional and Trellis Codes
Two new classes of skew codes over a finite field $\F$ are proposed, called skew convolutional codes and skew trellis codes.
Information Theory Information Theory
Secret Key Agreement with Physical Unclonable Functions: An Optimality Summary
A physical unclonable function (PUF) is a promising solution for local security in digital devices and this review gives the most relevant summary for information theorists, coding theorists, and signal processing community members who are interested in optimal PUF constructions.
Differential Privacy for Eye Tracking with Temporal Correlations
New generation head-mounted displays, such as VR and AR glasses, are coming into the market with already integrated eye tracking and are expected to enable novel ways of human-computer interaction in numerous applications.
Code Constructions for Physical Unclonable Functions and Biometric Secrecy Systems
The two-terminal key agreement problem with biometric or physical identifiers is considered.
Information Theory Cryptography and Security Multimedia Signal Processing Information Theory Probability
