Wednesday, August 20, 2014

Building Confidential and Efficient Query Services in the Cloud with RASP Data Perturbation




Building Confidential and Efficient Query Services in the Cloud with RASP Data Perturbation

ABSTRACT:
With the wide deployment of public cloud computing infrastructures, using clouds to host data query services has become an appealing solution for the advantages on scalability and cost-saving. However, some data might be sensitive that the data owner does not want to move to the cloud unless the data confidentiality and query privacy are guaranteed. On the other hand, a secured query service should still provide efficient query processing and significantly reduce the in-house workload to fully realize the benefits of cloud computing. We propose the random space perturbation (RASP) data perturbation method to provide secure and efficient range query and kNN query services for protected data in the cloud. The RASP data perturbation method combines order preserving encryption, dimensionality expansion, random noise injection, and random projection, to provide strong resilience to attacks on the perturbed data and queries. It also preserves multidimensional ranges, which allows existing indexing techniques to be applied to speedup range query processing. The kNN-R algorithm is designed to work with the RASP range query algorithm to process the kNN queries. We have carefully analyzed the attacks on data and queries under a precisely defined threat model and realistic security assumptions. Extensive experiments have been conducted to show the advantages of this approach on efficiency and security.

EXISTING SYSTEM:
Ø Requirements for constructing a practical query service in the cloud as the CPEL criteria: data confidentiality, query privacy, efficient query processing, and low in-house processing cost. Satisfying these requirements will dramatically increase the complexity of constructing query services in the cloud. Some related approaches have been developed to address some aspects of the problem.
Ø The crypto index and order preserving encryption (OPE) are vulnerable to the attacks. The enhanced crypto index approach puts heavy burden on the in-house infrastructure to improve the security and privacy.

DISADVANTAGES OF EXISTING SYSTEM:
Ø Do not satisfactorily addressing all aspects of Cloud.
Ø Increase the complexity of constructing query services in the cloud.
Ø Provide slow query services as a result of security and privacy assurance.

PROPOSED SYSTEM:
Ø We propose the random space perturbation (RASP) data perturbation method to provide secure and efficient range query and kNN query services for protected data in the cloud.
Ø The RASP data perturbation method combines order preserving encryption, dimensionality expansion, random noise injection, and random projection, to provide strong resilience to attacks on the perturbed data and queries.

ADVANTAGES OF PROPOSED SYSTEM:
Ø The RASP perturbation is a unique combination of OPE, dimensionality expansion, random noise injection, and random projection, which provides strong confidentiality guarantee.

Ø The RASP approach preserves the topology of multi-dimensional range in secure transformation, which allows indexing and efficiently query processing.

Ø  The proposed service constructions are able to minimize the in-house processing workload because of the low perturbation cost and high precision query results. This is an important feature enabling practical cloud-based solutions.







SYSTEM ARCHITECTURE:

SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:

Ø System                          :         Pentium IV 2.4 GHz.
Ø Hard Disk                      :         40 GB.
Ø Floppy Drive                 :         1.44 Mb.
Ø Monitor                         :         15 VGA Colour.
Ø Mouse                            :         Logitech.
Ø Ram                               :         512 Mb.



SOFTWARE REQUIREMENTS:

Ø Operating system           :         Windows XP/7.
Ø Coding Language :         JAVA/J2EE
Ø IDE                      :         Netbeans 7.4
Ø Database              :         MYSQL

REFERENCE:
Huiqi Xu, Shumin Guo, and Keke Chen,“Building Confidential and Efficient Query Services in the Cloud with RASP Data Perturbation”, IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 26, NO. 2, FEBRUARY 2014.