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Home / programming language / Online Java Projects / Privacy-preserving Verifiable Set Operation in Big Data for Cloud-assisted Mobile Crowdsourcing
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Privacy-preserving Verifiable Set Operation in Big Data for Cloud-assisted Mobile Crowdsourcing

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SKU: PROJ7824 Categories: 2017 Projects, Final Year Projects, Big Data Projects, Online Java Projects Tags: Android Project, Asp.Net Project, C# Project, Computer Engineering Final Year Projects, Computer Science Final Year Projects, Electronics Projects Engineering Students Final Year, Engineering Student Project Ideas, Final Semester Projects, Final Year Project Center, Final Year Projects, ieee ECE Projects, ieee EEE Projects, ieee Final Year Projects, IEEE Projects, ieee Projects Networking, Image Processing Projects, J2EE Project, Java Project, Java Project Code, Matlab Project, PHP Project, Students Projects
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Privacy-preserving Verifiable Set Operation in BigData for Cloud-assisted Mobile Crowd sourcing

Abstract—Abstract The ubiquity of smartphones makes the mobile crowdsourcing possible, where the requester (task owner) can crowdsource data from the workers (smartphone users) 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. In this work, we propose to use cloud to compute set operation for the requester, at the same time workers’ data privacy and identities privacy are well preserved. Besides, the requester can verify the correctness of set operation results.We also extend our scheme to support data preprocessing, with which invalid data can be excluded before data analysis. By using batch verification and data update methods, the proposed scheme greatly reduces the computational cost. Extensive performance analysis and experiment-based on real cloud system have shown both the feasibility and efficiency of our proposed scheme.< final year projects >

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