Secure Hypersphere Range Query on Encrypted Data

by   Gagandeep Singh, et al.

Spatial queries like range queries, nearest neighbor, circular range queries etc. are the most widely used queries in the location-based applications. Building secure and efficient solutions for these queries in the cloud computing framework is critical and has been an area of active research. This paper focuses on the problem of Secure Circular Range Queries (SCRQ), where client submits an encrypted query (consisting of a center point and radius of the circle) and the cloud (storing encrypted data points) has to return the points lying inside the circle. The existing solutions for this problem suffer from various disadvantages such as high processing time which is proportional to square of the query radius, query generation phase which is directly proportional to the number of points covered by the query etc. This paper presents solution for the above problem which is much more efficient than the existing solutions. Three protocols are proposed with varying characteristics. It is shown that all the three protocols are secure. The proposed protocols can be extended to multiple dimensions and thus are able to handle Secure Hypersphere Range Queries (SHRQ) as well. Internally the proposed protocols use pairing-based cryptography and a concept of lookup table. To enable the efficient use of limited size lookup table, a new storage scheme is presented. The proposed storage scheme enables the protocols to handle query with much larger radius values. Using the SHRQ protocols, we also propose a mechanism to answer the Secure range Queries. Extensive performance evaluation has been done to evaluate the efficiency of the proposed protocols



There are no comments yet.


page 1

page 2

page 3

page 4


Secure and Efficient Skyline Queries on Encrypted Data

Outsourcing data and computation to cloud server provides a cost-effecti...

Secure k-NN as a Service Over Encrypted Data in Multi-User Setting

To securely leverage the advantages of Cloud Computing, recently a lot o...

Panda: Partitioned Data Security on Outsourced Sensitive and Non-sensitive Data

Despite extensive research on cryptography, secure and efficient query p...

Dynamic Skyline Queries on Encrypted Data Using Result Materialization

Skyline computation is an increasingly popular query, with broad applica...

Probabilistic Counting in Uncertain Spatial Databases using Generating Functions

Location data is inherently uncertain for many reasons including 1) impr...

An Efficient Secure Dynamic Skyline Query Model

It is now cost-effective to outsource large dataset and perform query ov...

Concealer: SGX-based Secure, Volume Hiding, and Verifiable Processing of Spatial Time-Series Datasets

This paper proposes a system, entitled Concealer that allows sharing tim...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.