Multi-objective optimization based privacy preserving distributed data mining in Peer-to-Peer networks
Shared by Kanishka Bhaduri, updated on Nov 17, 2010
Summary
- Author(s) :
- Kamalika Das, Kanishka Bhaduri, H. Kargupta
- Abstract
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.
- Publication Name
- Multi-objective optimization based privacy preserving distributed data mining in Peer-to-Peer networks
- Publication Location
- Peer-to-Peer Netw. Applications (PPNA)
- Year Published
- 2011
Files
Discussions
Kanishka's Projects (4)
-
-
Aviation Safety Technology Portal ...
15 members
-
-
Need help?
Visit our help center