Abstract

Due to the overwhelming flow of information in many data stream applications, many companies may not be willing to acquire the necessary resources for deploying a Data Stream Management System (DSMS), choosing, alternatively, to outsource the data stream and the desired computations to a third-party. But data outsourcing and remote computations intrinsically raise issues of trust, making outsourced query assurance on data streams a problem with important practical implications. Consider a setting where a continuous “GROUP BY, SUM ” query is processed using a remote, untrusted server. A client with limited processing capabilities observing exactly the same stream as the server, registers the query on the server’s DSMS and receives results upon request. The client wants to verify the integrity of the results using significantly fewer resources than evaluating the query locally. Towards that goal, we propose a probabilistic verification algorithm for selection and aggregate/group-by queries, that uses constant space irrespective of the result-set size, has low update cost per stream element, and can have arbitrarily small probability of failure. We generalize this algorithm to allow some tolerance on the number of erroneous groups detected, in order to support semantic load shedding on the server. We also discuss the hardness of supporting random load shedding. Finally, we implement our techniques and perform an empirical evaluation using live network traffic. 1

Citations