Privacy Preservation in Dynamic Bigdata for Verification and Correctness (International Journal of Engineering and Computer Science, 2017)
Mobile devices makes the mobile crowd sourcing is possible, since the mobile devices are everywhere in a tourism(theme)/network if a requester(travel agency) crowd, sources the data from worker(tourist). However data collection aggregation and data analysis have become challenging problems for requester when the volume of data is huge which is categorized as Bigdata. The data analysis includes set operations like intersection, union, and complementation for filtering redundant data and pre-processing raw data. theme is a necessity of data exchange between the worker, requester for better analysis of the interested worker. But workers may not be willing to participate if the privacy of their sensing data and identity are not well preserved in the untrusted cloud. Hence, the proposed work, establishes the usage of cloud to compute the intersect operation between the requester and the workers data. Also preserves the workers identity and accessible data. This paper says that use of cloud to compute set operation for the requester, at the same time workers data privacy and identity privacy are well preserved. And also, the requester can verify the correctness of set operation results on the dataset sourced by workers and send to the cloud. With this batch verification and data update are comparatively increased and reduce computational costs of the system.