麻豆传媒AV

New Results on the Storage-Retrieval Tradeoff in Private Information Retrieval Systems

Submitted by admin on Mon, 10/28/2024 - 01:24

In a private information retrieval (PIR) system, the user needs to retrieve one of the possible messages from a set of storage servers, but wishes to keep the identity of the requested message private from any given server. Existing efforts in this area have made it clear that the efficiency of the retrieval will be impacted significantly by the amount of the storage space allowed at the servers. In this work, we consider the tradeoff between the storage cost and the retrieval cost.

Covert Communication Over the Poisson Channel

Submitted by admin on Mon, 10/28/2024 - 01:24

We consider the problem of communication over a continuous-time Poisson channel subject to a covertness constraint: The relative entropy between the output distributions when a message is transmitted and when no input is provided must be small. In the absence of both bandwidth and peak-power constraints, we show the covert communication capacity of this channel, in nats per second, to be infinity. When a peak-power constraint is imposed on the input, the covert communication capacity becomes zero, and the 鈥渟quare-root scaling law鈥 applies.

CodedPrivateML: A Fast and Privacy-Preserving Framework for Distributed Machine Learning

Submitted by admin on Mon, 10/28/2024 - 01:24

How to train a machine learning model while keeping the data private and secure? We present CodedPrivateML, a fast and scalable approach to this critical problem. CodedPrivateML keeps both the data and the model information-theoretically private, while allowing efficient parallelization of training across distributed workers. We characterize CodedPrivateML鈥檚 privacy threshold and prove its convergence for logistic (and linear) regression.