Secure multiparty privacy preserving data aggregation by modular arithmetic

The increasing ability to track and collect large amounts of data with the use of current hardware and software technology has lead to immense challenge and consequent interest in the development of data mining algorithms which preserve user security and privacy in a large distributed system. Secure data aggregation with privacy preserving feature is a demanding task. Privacy preservation is becoming a necessity for data generated for individual purpose as well as for organizational purpose. In this paper, we develop a scheme for secure multiparty data aggregation with the help of modular arithmetic concept. Specifically, we consider a scenario in which two or more parties owning confidential data need to share only for aggregation purpose to a third party, without revealing any unnecessary information. More generally, data aggregation needs to take place by the server or aggregator without acquiring the content of the individual data. Our work is motivated by the need to both protect privileged information and confidentiality. We have shown through simulation results the efficacy of our scheme and compare the result with one of the established scheme.