Achieving k-anonymity privacy protection for data streams based on grey relational analysis
Kun Guo · Journal of Northeast Petroleum University · 2012
Data streams have the features of potential infinity,fast flowing and frequent variation,which are quite different from static data.Protecting the privacy in a data stream meets many new problems.An anonymity algorithm based on grey relational analysis for data streams is proposed in order to reduce information loss and computation time.The similarity between two tuples is described by the grey relational degree.K anonymized clusters are built upon the measure,which are used for data stream anonymization.The experiments conducted on the real data set demonstrate that the new method can achieve lower information loss and less runtime when compared with CASTLE.