Multi-objective optimization based privacy preserving distributed data mining in Peer-to-Peer networks
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.
Unlike 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,
the 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.
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Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| accrualPeriodicity | irregular |
| bureauCode |
[ "026:00" ] |
| contactPoint |
{ "fn": "Kanishka Bhaduri", "@type": "vcard:Contact", "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. Unlike 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, the 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", "title": "Multi-objective optimization.pdf", "format": "PDF", "mediaType": "application/pdf", "description": "Multi-objective optimization.pdf", "downloadURL": "https://c3.nasa.gov/dashlink/static/media/publication/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 |
{ "name": "Dashlink", "@type": "org:Organization" } |
| title | Multi-objective optimization based privacy preserving distributed data mining in Peer-to-Peer networks |
