IEEE transactions on privacy最新文献

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Blockchain Based Secure Federated Learning With Local Differential Privacy and Incentivization. 基于区块链的安全联盟学习与本地差异化隐私和激励。

IEEE transactions on privacy Pub Date : 2024-01-01 Epub Date: 2024-11-08 DOI: 10.1109/tp.2024.3487819

Saptarshi DE Chaudhury, Likhith Reddy Morreddigari, Matta Varun, Tirthankar Sengupta, Sandip Chakraborty, Shamik Sural, Jaideep Vaidya, Vijayalakshmi Atluri

{"title":"Blockchain Based Secure Federated Learning With Local Differential Privacy and Incentivization.","authors":"Saptarshi DE Chaudhury, Likhith Reddy Morreddigari, Matta Varun, Tirthankar Sengupta, Sandip Chakraborty, Shamik Sural, Jaideep Vaidya, Vijayalakshmi Atluri","doi":"10.1109/tp.2024.3487819","DOIUrl":"10.1109/tp.2024.3487819","url":null,"abstract":"Interest in supporting Federated Learning (FL) using blockchains has grown significantly in recent years. However, restricting access to the trained models only to actively participating nodes remains a challenge even today. To address this concern, we propose a methodology that incentivizes model parameter sharing in an FL setup under Local Differential Privacy (LDP). The nodes that share less obfuscated data under LDP are awarded higher quantum of tokens, which they can later use to obtain session keys for accessing encrypted model parameters updated by the server. If one or more of the nodes do not contribute to the learning process by sharing their data, or share only highly perturbed data, they earn less number of tokens. As a result, such nodes may not be able to read the new global model parameters if required. Local parameter sharing and updating of global parameters are done using the distributed ledger of a permissioned blockchain, namely HyperLedger Fabric (HLF). Being a blockchain-based approach, the risk of a single point of failure is also mitigated. Appropriate chaincodes, which are smart contracts in the HLF framework, have been developed for implementing the proposed methodology. Results of an extensive set of experiments firmly establish the feasibility of our approach.","PeriodicalId":519971,"journal":{"name":"IEEE transactions on privacy","volume":"1 ","pages":"31-44"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11722199/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142974496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

U.S.-U.K. PETs Prize Challenge: Anomaly Detection via Privacy-Enhanced Federated Learning. 美英 PETs 奖挑战赛：通过隐私增强联合学习进行异常检测。

IEEE transactions on privacy Pub Date : 2024-01-01 Epub Date: 2024-04-23 DOI: 10.1109/tp.2024.3392721

Hafiz Asif, Sitao Min, Xinyue Wang, Jaideep Vaidya

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引用次数: 0