Recursive Binary Identification with Differential Privacy and Data Tampering Attacks

TL;DR

This paper develops recursive algorithms for parameter estimation in sensor networks that incorporate differential privacy and are robust against data tampering, ensuring convergence and security in insecure communication environments.

Contribution

It introduces a unified recursive estimation framework that combines differential privacy and tampering attack resilience, extending to multi-agent systems.

Findings

Algorithms achieve almost sure and mean-square convergence.

Differential privacy impacts convergence rate.

Effective in multi-agent sensor networks.

Abstract

In this paper, we consider the parameter estimation in a bandwidth-constrained sensor network communicating through an insecure medium. The sensor performs a local quantization, and transmits a 1-bit message to an estimation center through a wireless medium where the transmission of information is vulnerable to attackers. Both eavesdroppers and data tampering attackers are considered in our setting. A differential privacy method is used to protect the sensitive information against eavesdroppers. Then, a recursive projection algorithm is proposed such that the estimation center achieves the almost sure convergence and mean-square convergence when quantized measurements, differential privacy, and data tampering attacks are considered in a uniform framework. A privacy analysis including the convergence rate with privacy or without privacy is given. Further, we extend the problem to multi-agent systems. For this case, a distributed recursive projection algorithm is proposed with guaranteed almost sure and mean square convergence. A simulation example is provided to illustrate the effectiveness of the proposed algorithms.