Privacy-preserving Verifiable Set Operation in Big Data for Cloud-assisted Mobile Crowdsourcing
The ubiquity of smartphones makes the mobile crowdsourcing possible, where the requester can crowdsource data from the workers by using their sensor-rich mobile devices. However, data collection, data aggregation, and data analysis have become challenging problems for a resource constrained requester when data volume is extremely large, i.e., big data. In particular to data analysis, set operations, including intersection, union, and complementation, exist in most big data analysis for filtering redundant data and preprocessing raw data. Facing challenges in terms of limited computation and storage resources, cloud-assisted approaches may serve as a promising way to tackle big data analysis issue. However, workers may not be willing to participate if the privacy of their sensing data and identity are not well preserved in the untrusted cloud.

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