Multi-objective optimization based privacy preserving distributed data mining in Peer-to-Peer networks
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.
Resources
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Description |
Link |
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33 |
Multi-objective optimization.pdf |
https://c3.nasa.gov/dashlink/static/media/publication/Multi-objective_optimization.pdf |
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33 |
Multi-objective optimization.pdf |
https://c3.nasa.gov/dashlink/static/media/publication/Multi-objective_optimization.pdf |