A PRACTICAL APPROACH SCHEMA OF PRIVACY PRESERVING IN DATA STREAMS

Abstract

Recently, data mining over transactional data streams has become an attractive research area. However, releasing raw transactional data streams, in which only explicit identifying information must be removed and may compromise individual privacy. Many privacy-preserving approaches have been proposed for publishing static transactional data. Due to the characteristics of data streams, which must be processed quickly, static data anonymization methods cannot be directly applied to data streams. Shadow Coding is use to preserve the privacy in data transmission and ensure the recovery in data collection, it achieves privacy preserving computation in a data-recoverable, efficient, and scalable way. This paper provide practical techniques to make Shadow Coding efficient and safe in data streams.