Privacy Preserving Using Distributed K means Clustering for Arbitrarily Partitioned Data

  Neha B. Jinwala,  Gordhan B. Jethava

Advances in computer networking and database technologies have enabled the collection and storage of large quantities of data, also the freedom and transparency of information flow on the Internet has heightened concerns of privacy. Nowadays the scenario of one centralized database that maintains all the data is difficult to achieve due to different reasons including physical, geographical restrictions and size of the data itself. The data is normally maintained by more than one organization, each of which aims at keeping its information stored in the databases privately, thus, privacy-preserving techniques and protocols are designed to perform data mining on distributed data when privacy is highly concerned. Cluster analysis is a frequently used data mining task which aims at decomposing or partitioning a usually multivariate data set into groups such that the data objects in one group are most similar to each other. Distributed data mining is concerned with the computation of data that is distributed among multiple participants. Privacy preserving distributed data mining allows the cooperative computation of data without parties revealing their individual data. The work focuses on arbitrarily partitioned data which is a generalization of horizontally partitioned data and vertically partitioned data along with Shamir’s Secret Sharing Schemes which was designed with the goal of achieving complete privacy for secure computation and communication between different parties. At the end of the work one can conclude that one achieves privacy with minimum or no leakage of the data thus satisfying the security constraint.

Keywords- Centralized K-means, Distributed K-means, Privacy preserving clustering, Shamir’s Secret Sharing

Unique Identification Number - IJEDR1402161
Page Number(s) - 2291-2295
Pubished in - Volume 2 | Issue 2 | June 2014
DOI (Digital Object Identifier) -   
Publisher - IJEDR (ISSN - 2321-9939)

  Neha B. Jinwala,  Gordhan B. Jethava,   "Privacy Preserving Using Distributed K means Clustering for Arbitrarily Partitioned Data", International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.2, Issue 2, pp.2291-2295, June 2014, Available at :http://www.ijedr.org/papers/IJEDR1402161.pdf