Precision-Bounded Access Control Using Sliding-Window Query Views for Privacy-Preserving Data Streams

Abstract

Access control mechanisms and Privacy Protection Mechanisms (PPM) have been proposed for data streams. The access control for a data stream allows roles access to tuples satisfying an authorized predicate sliding-window query. Sharing the sensitive stream data without PPM can compromise the privacy. The PPM meets privacy requirements, e.g., $k$ -anonymity or $l$ -diversity by generalization of stream data. Imprecision introduced by generalization can be reduced by delaying the publishing of stream data. However, the delay in sharing the stream tuples to achieve better accuracy can lead to false-negatives if the tuples are held by PPM while the query predicate is evaluated. Administrator of an access control policy defines the imprecision bound for each query. The challenge for PPM is to optimize the delay in publishing of stream data so that the imprecision bound for the maximum number of queries is satisfied. We formulate the precision-bounded access control for privacy-preserving data streams problem, present the hardness results, provide an anonymization algorithm, and conduct experimental evaluation of the proposed algorithm. Experiments demonstrate that the proposed heuristic provides better precision for a given data stream access control policy as compared to the minimum or maximum delay heuristics proposed in existing literature.