With the increasing use of P2P (peer-to-peer) network technology in everyday services, the issue of privacy protection has gained considerable importance. This paper describes a method to realize an anonymity-conscious P2P data sharing network. The proposed network allows users to extract data possessed by other users who have similar profiles, thereby providing them with a collaborative filtering-based data recommendation. In the proposed P2P network protocol, bogus user profiles are distributed intentionally throughout the network to protect users' anonymity without harming the overall effectiveness of the data exchange. We conduct a series of simulations to prove that our proposed method protects the profile's privacy and performs efficient data exchange.
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Networks and Communications