dorsal/arxiv
View SchemaRecursive Binary Identification with Differential Privacy and Data Tampering Attacks
| Authors | Jimin Wang, Jieming Ke, Jin Guo, Yanlong Zhao |
|---|---|
| Categories | |
| ArXiv ID | 2601.07608vv1 |
| URL | https://arxiv.org/abs/2601.07608 |
| License | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
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.
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"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.",
"arxiv_id": "2601.07608",
"authors": [
"Jimin Wang",
"Jieming Ke",
"Jin Guo",
"Yanlong Zhao"
],
"categories": [
"eess.SY",
"cs.SY"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"title": "Recursive Binary Identification with Differential Privacy and Data Tampering Attacks",
"url": "https://arxiv.org/abs/2601.07608",
"version": "v1"
},
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"source": {
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