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.