Wenrui Yu, Richard Heusdens, Jun Pang, Qiongxiu Li
{"title":"无精度损失的隐私保护分布式最大共识","authors":"Wenrui Yu, Richard Heusdens, Jun Pang, Qiongxiu Li","doi":"arxiv-2409.10226","DOIUrl":null,"url":null,"abstract":"In distributed networks, calculating the maximum element is a fundamental\ntask in data analysis, known as the distributed maximum consensus problem.\nHowever, the sensitive nature of the data involved makes privacy protection\nessential. Despite its importance, privacy in distributed maximum consensus has\nreceived limited attention in the literature. Traditional privacy-preserving\nmethods typically add noise to updates, degrading the accuracy of the final\nresult. To overcome these limitations, we propose a novel distributed\noptimization-based approach that preserves privacy without sacrificing\naccuracy. Our method introduces virtual nodes to form an augmented graph and\nleverages a carefully designed initialization process to ensure the privacy of\nhonest participants, even when all their neighboring nodes are dishonest.\nThrough a comprehensive information-theoretical analysis, we derive a\nsufficient condition to protect private data against both passive and\neavesdropping adversaries. Extensive experiments validate the effectiveness of\nour approach, demonstrating that it not only preserves perfect privacy but also\nmaintains accuracy, outperforming existing noise-based methods that typically\nsuffer from accuracy loss.","PeriodicalId":501034,"journal":{"name":"arXiv - EE - Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Privacy-Preserving Distributed Maximum Consensus Without Accuracy Loss\",\"authors\":\"Wenrui Yu, Richard Heusdens, Jun Pang, Qiongxiu Li\",\"doi\":\"arxiv-2409.10226\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In distributed networks, calculating the maximum element is a fundamental\\ntask in data analysis, known as the distributed maximum consensus problem.\\nHowever, the sensitive nature of the data involved makes privacy protection\\nessential. Despite its importance, privacy in distributed maximum consensus has\\nreceived limited attention in the literature. Traditional privacy-preserving\\nmethods typically add noise to updates, degrading the accuracy of the final\\nresult. To overcome these limitations, we propose a novel distributed\\noptimization-based approach that preserves privacy without sacrificing\\naccuracy. Our method introduces virtual nodes to form an augmented graph and\\nleverages a carefully designed initialization process to ensure the privacy of\\nhonest participants, even when all their neighboring nodes are dishonest.\\nThrough a comprehensive information-theoretical analysis, we derive a\\nsufficient condition to protect private data against both passive and\\neavesdropping adversaries. Extensive experiments validate the effectiveness of\\nour approach, demonstrating that it not only preserves perfect privacy but also\\nmaintains accuracy, outperforming existing noise-based methods that typically\\nsuffer from accuracy loss.\",\"PeriodicalId\":501034,\"journal\":{\"name\":\"arXiv - EE - Signal Processing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-09-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - EE - Signal Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2409.10226\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - EE - Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.10226","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Privacy-Preserving Distributed Maximum Consensus Without Accuracy Loss
In distributed networks, calculating the maximum element is a fundamental
task in data analysis, known as the distributed maximum consensus problem.
However, the sensitive nature of the data involved makes privacy protection
essential. Despite its importance, privacy in distributed maximum consensus has
received limited attention in the literature. Traditional privacy-preserving
methods typically add noise to updates, degrading the accuracy of the final
result. To overcome these limitations, we propose a novel distributed
optimization-based approach that preserves privacy without sacrificing
accuracy. Our method introduces virtual nodes to form an augmented graph and
leverages a carefully designed initialization process to ensure the privacy of
honest participants, even when all their neighboring nodes are dishonest.
Through a comprehensive information-theoretical analysis, we derive a
sufficient condition to protect private data against both passive and
eavesdropping adversaries. Extensive experiments validate the effectiveness of
our approach, demonstrating that it not only preserves perfect privacy but also
maintains accuracy, outperforming existing noise-based methods that typically
suffer from accuracy loss.