{"@type": "dcat:Dataset", "accessLevel": "public", "accrualPeriodicity": "irregular", "bureauCode": ["026:00"], "contactPoint": {"@type": "vcard:Contact", "fn": "Kanishka Bhaduri", "hasEmail": "mailto:kanishka.bhaduri-1@nasa.gov"}, "description": "This paper proposes a scalable, local privacy preserving algorithm for distributed Peer-to-Peer (P2P) data aggregation useful for many advanced data mining/analysis tasks such as average/sum computation, decision tree induction, feature selection, and more.\r\nUnlike most multi-party privacy-preserving data mining algorithms, this approach works in an asynchronous manner through local interactions and it is highly scalable. It particularly deals with the distributed computation of the sum of a set of numbers stored at different peers in a P2P network in the context of a P2P web mining application. The proposed optimization based privacy-preserving technique for computing the sum allows different peers to specify different privacy requirements without having to adhere to a global set of parameters for the chosen privacy model. Since distributed sum computation is a frequently used primitive,\r\nthe proposed approach is likely to have significant impact on many data mining tasks such as multi-party privacy-preserving clustering, frequent itemset mining, and statistical aggregate computation.", "distribution": [{"@type": "dcat:Distribution", "description": "Multi-objective optimization.pdf", "downloadURL": "https://c3.nasa.gov/dashlink/static/media/publication/Multi-objective_optimization.pdf", "format": "PDF", "mediaType": "application/pdf", "title": "Multi-objective optimization.pdf"}], "identifier": "DASHLINK_262", "issued": "2010-11-17", "keyword": ["ames", "dashlink", "nasa"], "landingPage": "https://c3.nasa.gov/dashlink/resources/262/", "modified": "2025-03-31", "programCode": ["026:029"], "publisher": {"@type": "org:Organization", "name": "Dashlink"}, "title": "Multi-objective optimization based privacy preserving distributed data mining in Peer-to-Peer networks"}