An Approach for Privacy Preservation of Distributed Data in Peer-to-Peer Network using Multiparty Computation
Use of technology for data collection and
analysis has seen an unprecedented growth in the last
couple of decades. Individuals and organizations
generate huge amount of data through everyday
activities. This data is either centralized for pattern
identification or mined in a distributed fashion for
efficient knowledge discovery and collaborative
computation. This has raised serious concerns about
privacy issues. The data mining community has
responded to this challenge by developing a new breed
of algorithms that are privacy preserving. The main
objective of data mining is to extract the identified
pattern for efficient knowledge discovery with
centralized or decentralized collaborative computation.
This paper focuses on developing secure computational
model for preserving the privacy of the distributed data
by performing multiparty computation in peer-to-peer
network. However this approach requires that
participating parties are attached to the coordinator of the
peer-to-peer network through a specified path and
maintain privacy by performing certain application
specific computation on their local site. The computation
is performed by taking the distributed data-set of a
particular scenario through centralized and decentralized
fashion.
Keywords: Distributed data, distributed data mining, privacy preservation, secure evaluation, peer-to-peer network, perturbation
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