Precision-bounded Access Control for Privacy-preserving Data Streams

Access control mechanisms and Privacy Protection Mechanisms (PPM) have been proposed for data streams. The access control for data stream allows roles access to tuples satisfying an authorized predicate sliding-window query. When the sensitive stream data is shared without a PPM the privacy can be compromised. The PPM meets privacy requirements, e.g., k-anonymity or l-diversity by generalization of stream data. This 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 slide out of the window when a sliding-window query predicate is evaluated for access control mechanism. To set a threshold on the loss of precision, access control mechanism defines the imprecision bound for each query. The challenge is to optimize the time duration for which the stream data is held by PPM so that the imprecision bounds for the maximum possible number of queries are met. In our formulation of the aforementioned problem we 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 as compared to the minimum or maximum delay heuristics.