Privacy Preservation in Streaming Data Collection

Wee Siong Ng, Huayu Wu, Wei Wu, Shili Xiang, Kian-Lee Tan

Abstract: Big data management and analysis has become a hot topic in academic and industrial research. In fact, a large portion of big data in service today are initially streaming data. To preserve the privacy of such data that are collected from data streams, the most efficient way is to control the process of data collection according to corresponding privacy polices. In this paper, we design a framework to support data stream management with privacy-preserving capabilities. In particular, we focus on two premier principles of data privacy, limited disclosure and limited collection. With these two principles guaranteed, the archived data will not necessarily be checked for privacy protection, before analysis and other operations can be done.