We consider the distributed access enforcement problem for Role-Based Access Control (RBAC) systems. Such enforcement has become important with RBAC’s increasing adoption, and the proliferation of data that needs to be protected. Our particular interest is in the evaluation of a new data structure that has recently been proposed for enforcement: the Cascade Bloom Filter. The Cascade Bloom Filter is an extension of the Bloom filter, and provides for time- and space-efficient encodings of sets. We compare the Cascade Bloom Filter to the Bloom Filter, and another approach called Authorization Recycling that has been proposed for distributed access enforcement in RBAC. One of the challenges we address is the lack of a benchmark: we propose and justify a benchmark for the assessment. Also, we adopt a statistically rigorous approach for empirical assessment from recent work. We present our results for time- and space-efficiency based on our benchmark. We demonstrate that, of the three data structures that we consider, the Cascade Bloom Filter scales the best with the number of RBAC sessions from the standpoints of time- and space-efficiency.

We consider the distributed access enforcement problem for Role-Based Access Control (RBAC) systems. Such enforcement has become important with RBAC’s increasing adoption, and the proliferation of data that needs to be protected. Our particular interest is in the evaluation of a new data structure that has recently been proposed for enforcement: the Cascade Bloom Filter. The Cascade Bloom Filter is an extension of the Bloom filter, and provides for time- and space-efficient encodings of sets. We compare the Cascade Bloom Filter to the Bloom Filter, and another approach called Authorization Recycling that has been proposed for distributed access enforcement in RBAC. One of the challenges we address is the lack of a benchmark: we propose and justify a benchmark for the assessment. Also, we adopt a statistically rigorous approach for empirical assessment from recent work. We present our results for time- and space-efficiency based on our benchmark. We demonstrate that, of the three data structures that we consider, the Cascade Bloom Filter scales the best with the number of RBAC sessions from the standpoints of time- and space-efficiency.