PROJECT TITLE :

FastGeo: Efficient Geometric Range Queries on Encrypted Spatial Data - 2017

ABSTRACT:

Spatial information have wide applications, e.g., location-based services, and geometric range queries (i.e., finding points within geometric areas, e.g., circles or polygons) are one in every of the elemental search functions over spatial knowledge. The rising demand of outsourcing knowledge is moving massive-scale datasets, including giant-scale spatial datasets, to public clouds. Meanwhile, because of the priority of insider attackers and hackers on public clouds, the privacy of spatial datasets should be cautiously preserved while querying them at the server facet, especially for location-based and medical usage. During this paper, we have a tendency to formalize the concept of Geometrically Searchable Encryption, and propose an efficient theme, named FastGeo, to shield the privacy of shoppers’ spatial datasets stored and queried at a public server. With FastGeo, that could be a novel 2-level hunt for encrypted spatial data, an honest-however-curious server can efficiently perform geometric vary queries, and properly come data points that are within a geometrical range to a shopper without learning sensitive data points or this non-public query. FastGeo supports arbitrary geometric areas, achieves sublinear search time, and allows dynamic updates over encrypted spatial datasets. Our scheme is provably secure, and our experimental results on real-world spatial datasets in cloud platform demonstrate that FastGeo can boost search time over one hundred times.


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