Research on data streams publishing of privacy preserving

Data streams contain a lot of client information that need to be carefully managed to protect privacy of clients. Most of existing privacy preserving methods, such as k-anonymity, was designed for static data sets. However these methods can not be applied on data streams directly. Moreover, in dada streams applications, there is a need to offer strong guarantees on maximum allowed delay between incoming data and its anonymous output. This paper presents a novel weak clustering based data streams k-anonymity method for data publishing. Three advantages make its practical: first, little processing time for each tuple of data steam. Second, demand less memory. Last, both privacy preserving and utility of anonymous data are considered carefully. Theoretical analysis and experimental results show that the method is efficient and effective.