Peer-to-Peer Data Mining, Privacy Issues, and Games

Abstract

Abstract. Peer-to-Peer (P2P) networks are gaining increasing popularity in many distributed applications such as file-sharing, network storage, web caching, searching and indexing of relevant documents and P2P network-threat analysis. Many of these applications require scalable analysis of data over a P2P network. This paper starts by offering a brief overview of distributed data mining applications and algorithms for P2P environments. Next it discusses some of the privacy concerns with P2P data mining and points out the problems of existing privacy-preserving multi-party data mining techniques. It further points out that most of the nice assumptions of these existing privacy preserving techniques fall apart in real-life applications of privacy-preserving distributed data mining (PPDM). The paper offers a more realistic formulation of the PPDM problem as a multi-party game and points out some recent results.

COBS: A tool for collaborative browsing and search on the web

“…The main challenges in this respect are to ensure a reliable and efficient data analysis, e.g. using replication schemes [3], and to consider privacy concerns of users when storing and analyzing the data [4].…”

Privacy-Preserving Data Mining in Peer to Peer Networks

“…The combined bandwidth is used to transfer data [1]. Furthermore it is seen that viruses are available on most of the programs in the form of music or video files which in turn can damage the user"s computer [2]. It is clear that there is no need for a client and server as each node acts as a peer on the network.…”

Anonymous Gossiping

“…Research on both these individual families of privacy preserving data analytics (PPDA) as well as hybridized solutions continue in full steam. Privacy preserving data mining in P2P environ-ments [4] has also gained considerable attention in recent years.…”