Preserving data privacy in outsourcing data aggregation services

Abstract

Advances in distributed service-oriented computing and Internet technology have formed a strong technology push for outsourcing and information sharing. There is an increasing need for organizations to share their data across organization boundaries both within the country and with countries that may have lesser privacy and security standards. Ideally, we wish to share certain statistical data and extract the knowledge from the private databases without revealing any additional information of each individual database apart from the aggregate result that is permitted. In this article, we describe two scenarios for outsourcing data aggregation services and present a set of decentralized peer-to-peer protocols for supporting data sharing across multiple private databases while minimizing the data disclosure among individual parties. Our basic protocols include a set of novel probabilistic computation mechanisms for important primitive data aggregation operations across multiple private databases such as max, min, and top k selection. We provide an analytical study of our basic protocols in terms of precision, efficiency, and privacy characteristics. Our advanced protocols implement an efficient algorithm for performing k NN classification across multiple private databases. We provide a set of experiments to evaluate the proposed protocols in terms of their correctness, efficiency, and privacy characteristics.