Efficient and Privacy-Preserving Polygons Spatialquery Framework for Location-Based Services

Efficient and Privacy-preserving Polygons SpatialQuery Framework for Location-based Services

ABSTRACT:

With the pervasiveness of mobile devices and thedevelopment of wireless communication technique, location-basedservices (LBS) have made our life more convenient, and thepolygons spatial query, which can provide more flexible LBS, hasattracted considerable interest recently. However, the flourish ofpolygons spatial query still faces many challenges including thequery information privacy. In this paper, we present an efficientand privacy-preserving polygons spatial query framework forlocation-based services, called Polaris. With Polaris, the LBSprovider outsources the encrypted LBS data to cloud server,and the registered user can query any polygon range to getaccurate LBS results without divulging his/her query informationto the LBS provider and cloud server. Specifically, an efficientspecial polygons spatial query algorithm (SPSQ) over ciphertextis constructed, based on an improved homomorphic encryptiontechnology over composite order group. With SPSQ, Polaris cansearch outsourced encrypted LBS data in cloud server by theencrypted request, and respond the encrypted polygons spatialquery results accurately. Detailed security analysis shows thatthe proposed Polaris can resist various known security threats.In addition, performance evaluations via implementing Polaris onsmartphone and workstation with real LBS dataset demonstratePolaris’ effectiveness in term of real environment.

EXISTING SYSTEM:

The k-Anonymity technique, cloaking technique and homomorphic encryption techniques are introduced in LBS. Specifically, k-Anonymity ensures that a user cannot be identified with a probability at least 1/k through parting user location into groups each containing at least k users. Cloaking technique is extensively used to prevent the disclosure of user’s data through blurring user location into a cloaked spatial regions.

Kalnis et al. presented a framework for preventing location-based identity inference of users who issue spatial queries to Location Based Services, whose transformations based on the well established k-anonymity concept to compute exact answers for range and nearest neighbor search, without revealing the querysource.

Ku et al. provided network distance spatial query solutions algorithms for answering nearest neighbor queriesand range queries on spatial networks without revealing private information of the query initiator by utilizing k-anonymitymechanisms.

Vu et al. proposed a mechanism based on locality-sensitive hashing (LSH) to partition user locations into groups each containing at least k users (called spatial cloaks), which is shown to preserve both locality and k-anonymity, and devise an efficient algorithm to answer kNN queries.

DISADVANTAGES OF EXISTING SYSTEM:

Plenty of users’ sensitive information (such as location, interest, etc.) could be easily analyzed and revealed by the LBS provider.

If those sensitive information is obtained by an attacker who could determine users’ location and track them, this will open up many computer-aided crime possibilities (harassment, car theft, kidnapping, etc.).

Both k-Anonymity technique and cloaking technique bring heavy communication overhead to user side, which lead to much energy consumption on the mobile device.

In addition, traditional homomorphic encryption techniques, which can achieve data operations over encrypted data with low communication overhead, ensure that the probability of a user being identified is very low.

However, most of them requiremassive resource-consuming computation, which makes themnot quite suitable for the mobile device.

PROPOSED SYSTEM:

In this paper, we propose an efficient and privacy-preservingpolygons spatial query framework for location-based services,called Polaris. With Polaris, the LBS provider can outsourcetheir encrypted data to the cloud server, and users can queryany polygon range to get accurate encrypted LBS results incloud server with encrypted query information.

In addition, theproposed framework is characterized by protecting the users’query information privacy from the LBS provider and cloudserver, and keeping LBS provider’s sensitive data secret fromthe cloud server.

First, the proposed Polaris provides a privacy-preservingpolygons spatial query framework for LBS.

Since the users’ encrypted queries and the encrypted LBS data are processed in the cloud server during the process of polygons range spatial query, to minimize the privacy disclosure due to the analysis of cloud server, Polaris introduces an improved homomorphic encryption technology over composite order group and hash value sequences.

In addition, only registered users are allowed to query and obtain the desirable LBS data.

Second, the proposed Polaris provides an accurate polygons spatial query algorithm. We construct an efficient special polygon spatial query algorithm (SPSQ) over composite order group based on the 2DNF cryptosystem.

Specifically, through using the Pollard’s lambda method, SPSQ can quickly determine whether the target point within the polygon over ciphertext.

Third, Polaris provides efficient polygon spatial query services in real environment.

ADVANTAGES OF PROPOSED SYSTEM:

With Polaris, users can set the query range independently and keep his/her query polygons secret from the LBS provider and cloud server. Meanwhile, the LBS provider can also keep the data items secret from the outsourced cloud server.

We evaluate the performance of the proposed Polaris in terms of the computation complexity of the LBS provider, cloud server and users, and deploy Polaris in smartphone and workstation with a real LBS dataset.

Performance evaluation demonstrates that the proposed Polaris can provide an efficient privacy preserving spatial polygon query in real environment.

Minimize the privacy disclosure.

SYSTEM ARCHITECTURE:

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

System: Pentium Dual Core.

Hard Disk : 120 GB.

Monitor: 15’’ LED

Input Devices: Keyboard, Mouse

Ram:1 GB

SOFTWARE REQUIREMENTS:

Operating system : Windows 7.

Coding Language:JAVA/J2EE

Tool:Netbeans 7.2.1

Database:MYSQL

REFERENCE:

Hui Zhu, Member, IEEE, Fen Liu, and Hui Li, Member, IEEE, “Efficient and Privacy-preserving Polygons SpatialQuery Framework for Location-based Services”, IEEE INTERNET OF THINGS JOURNAL, 2017.