Recursive Binary Identification with Differential Privacy and Data Tampering Attacks
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.
Cite
@article{arxiv.2601.07608,
title = {Recursive Binary Identification with Differential Privacy and Data Tampering Attacks},
author = {Jimin Wang and Jieming Ke and Jin Guo and Yanlong Zhao},
journal= {arXiv preprint arXiv:2601.07608},
year = {2026}
}