Literature Survey on Buliding Confidential and Efficient Query Processing Using Rasp Methodology

ISSN 2350-1022
International Journal of Recent Research in Mathematics Computer Science and Information Technology
Vol. 2, Issue 1, pp: (84-89), Month: April 2015 – September 2015, Available at: www.paperpublications.org
Page | 84
Paper Publications
Literature Survey on Building Confidential and
Efficient Query Processing Using RASP
Methodology
Shreen Sumayya A. G1
, Aravind Gosh2
, Rajakumari3
1,2,3
Dept of CSE, Bharath University, Chennai, India
Abstract: Hosting data query services with the deployed cloud computing infrastructure increase the scalability
and high performance evaluations with low cost. However, some data owners might not be interested to the save
their in the cloud environment because of data confidentiality and query processing privacy should be guaranteed
by the cloud service providers. Secured Query should able to provide very high efficient of query processing and
also should reduce in – house workload. In this paper we proposed RASP data perturbation techniques combines
various objectives like random noise injection, dimensionality expansion, efficient encryption and random
projection, henceforth RASP methodology are also used to preserves multidimensional ranges. KNN – R algorithm
used to work with RASP range for processing KNN queries. The experimental result of our project carried out to
define realistic security and threat model approaches for improved efficient and security.
Keywords: KNN Query Services, Privacy Range Query, OPE (Order Preserving Encryption), OBDA (Ontology
Based Data Access), and CRT (Cryptographic Transformation).
1. INTRODUCTION
Hosting data and intensively querying the process in the cloud environment because there is the unique content of
scalability and low cost services providers. The services owners needs to pay for the amount time accessing the service
providers, this methodology is an very high attractive features, henceforth providing workloads within house
infrastructure, however the service providers able to lose the control over the information in the cloud services providers
like IBM, Microsoft etc can able to make database which is difficult to detect and prevent cloud infrastructure. While
there is a need for new approaches for preserving confidentiality and query privacy, therefore we should able to provide
high meaning process without slow query process.
Data owners should uses the cloud environment for maintaining in – house infrastructure, so there should be intricate
relationship between query privacy confidentiality and compatibility, economically of using the cloud. Complexity for
constructing the query services dramatically increases efficient processing of query in the real time, Query privacy, and
data confidentiality. Random Space Peturbation (RASP) is a new method we proposed in this paper for developing range
query and KNN query services in the cloud. The basic idea for the Random Space Perturbation is to transform the dataset
with multidimensional analysis with a combination of random project, random noise, expansion of dimensionality and
preserving order of encryption therefore utility of processing query ranges has been preserved, this transformation of the
data carried out securely in the form of polyhedral in the Random Space Perturbation (RASP) data space. The key
components used in the RASP framework includes unique combination of RASP perturbation with OPE (Order
Preserving Encryption). The proposed techniques used to minimize workload of in – house processing by the high query
precision and perturbation with low cost.

ISSN 2350-1022
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Paper Publications
The layout of the paper is as follows. In section 2, we address the above mentioned techniques and also give a brief on the
literature being reviewed for the same. Section 3, presents a comparative study of the various research works explored in
the previous section. Lastly, we concluded in section 4 and section 5 is provided references.
2. RELATED WORKS
In this paper [1] Random Space Perturbation (RASP) method used to host query servicing in the cloud environment
RASP used to meet CPEL criteria like efficient processing of query, Query Privacy, very low – in house workload and
high confidentiality data transformation of the basic requirements for the low – house workload used to benefit the cloud
computing and makes very accurate, efficient processing of query by the quality of query measure services. The
Perturbation of RASP has been the unique combination of random projection, noise injection, expansion of
dimensionality and OPE (Order Preserving Encryption) queries ranged to preserve perturbed space which allows efficient
query processing.
In this paper [2] the main components like Optique’s architectural descriptions gives out the information for the query
processing for the end users in order to pose the queries mapping management techniques and ontology methodology are
used for the installation. Components like query transformation used to rewrite the users queries in the cloud data servers.
Processing components and distributed query optimization used to execute transformation of query. Ontology Based Data
Access (OBDA) used for the execution of variable scalable access of data efficient query for the project to provide
generic type of architecture.
In this paper [3] cheaper commodity machines and very large amount of scalable database for the storage purpose
therefore cloud technology has been implemented for solutions. In the cloud data storage customer targeted for the data
which has been outsourced (i.e the data which is not stored and not retrieved any sort of the information from customers
own servers). From customers point of view the data should be accessed quickly and immediately without no time when
the users has given the request and also the data stored on the cloud server for the efficient and effective transformation of
query optimization. This paper helps to study about detail description of available query optimization techniques for the
retrieval of data from the cloud storage.
In this paper [4] Ranging the queries is the most frequently used for the outline data analytics process which providers
very much expensive for the data owners accessing cloud server. With the huge and high dimensionality of services like
cloud computing became outsourced for the large amount of data from the database services providers hence it maintain
effective and efficient amount of range query services, because of the outsourced service, the data owner who access the
cloud infrastructure and data rich storage applications. Mostly encryption based approaches for cloud access linear
scanning devices for the whole database which is usually in appropriated for the online data analytics for the huge amount
of database, hence very few encryption focuses in the vulnerable prior of knowledge.
In this paper [5] content sharing and collaborative services allows the data owner’s for sharing the private spatial data (eg.
Some important key points displayed in the internet, tagging various business data) with the authentication and authorized
users of the community. Spatial transformation of data done by the Enhansed HSD and Cryptographic Transformation
(CRT), which offeres high range of query processing, confidentiality and increases communication between cloud server
and local host. Data owner can choose any type of transformation technique which matches the requirements, trading
between query efficiency and data privacy acquired in the cloud computing.
In [6] the authors described a newly developed data management technique is used in the cloud environment for fencing
various cloud services providers, but most of the cloud servers are designed in a structured data management for accessing
host system management configuration. SQL query language cannot directly support the traditional database and newly
relational database methods; therefore a newly structured language has been created called RDMS (Relation Database
Management System) for overall query performances. This paper gives detail information about efficiency of storage data
in the cloud data management system. Efficient algorithm helps to address the operation of query processing a structured
data.
In [7] the authors study about that cloud computing will grow for the accounting of horizontal scalability of different
types of virtual machines which access over cloud server to the single virtual machine, hence there need and required of
application software for scale – up and scale – down process which includes pay – for – use licensing model. Newly

ISSN 2350-1022
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Paper Publications
developed infrastructure is needed for building up longer running of virtual machines over cloud infrastructure. This paper
helps us to know about operational cost for matching performances analysis and rewarding energy proportionality by
making some part of idle portions of the memory, network shall act in lower power mode of transformation.
In this paper [8] data perturbation a popular technique used in the cloud computing has been detailed studied data
perturbation used for accessing privacy data mining methodology. The major part of data perturbation is to balance
privacy and quality performance. Geometrical analysis of data perturbation consists of noise additions, translation
perturbation and accessing multidimensional dataset which is able to provide details about privacy guarantee for modeling
classification analog. This paper studies about preserving the accuracy model for the different types of classification
models which includes kernel, different types of operating system and virtual system management.
In this paper [9] Encryption has been well – established making or creating technology which can be used for protecting
sensitive types of data. Order – Preserving Encryption Scheme (OPES) used in the various types of numeric data by
comparing encrypted cloud data, the newly updated value added without any changes in the encryption values of other
service providers. Standard database values has been encrypted and easily integrated with database in the existing system.
This paper shows OPES performance where query processing is deployed in the production of cloud environment.
In this paper [10] rapidly advancing technology used in the network, internet technologies have been widely desperate and
used emerging cloud technology like “Software as the Service” for supporting industrial needs. “Database as a Service”
used to create various type of application like creating the database, storing the data in the database and retrieving the data
from cloud server from any part of the world at any time over the internet. Several types of challenges issued for the data
privacy. This paper gives details about two types of privacy issues: 1.The owner of the database should make agreement
with cloud service providers in order to protect thefts and hacking of the messages from the outsiders. 2. The data has to
be protected from the cloud service providers because they are also agency whom the data owners can trust with a point.
3. COMPARTIVE STUDY
We have analyzed the various research works on several parameters and presented their comparison in the table below.
Table1. COMPARISON OF VARIOUS RESEARCH WORKS
Sl.
No
TITLE AUTHO
R
ISSUES METHOD
USED
TOOLS/LANG ADVANTAGE/
DISADAVANTAGE
1 Building
Confidential
and Efficient
Query
Services in
the Cloud
with RASP
Data
Perturbation.
Huiq i Xu,
Shumin
Guo, and
Keke Chen
VOL. 26,
NO. 2,
February
2014.
RASP
(Random
Space
Perturbation).
OPE (Order
Preserving
Encryption).
CPEL Criteria.
Query Privacy.
Cloud
Computing.
Data
Transformation
tools.
Advantages:
1. Host query processing in the cloud.
2. Very low – in house work load had
been created.
Disadvantages:
1. Arranging the queries in the
preserved leads to the query
processing.
2 Distributed
Query
Processing
on the
Cloud: the
Optique
Point of
View
H.
Kllapi, D.
Bilidas, I.
Horrocks,
Y.
Ioannidis,
E.
Jiménez-
Ruiz, E.
Kharlamov
, M.
Koubaraki
s, D.
France. May
26-30, 2013
Query
Ontology Data
Access
(OBDA)
Mapping
management.
Ontology
Method.
Optique’s
architectural tool
Query
optimization.
Advantages:
1. Query processing for the end user
has been created successfully.
2. Queries can be rewritten in the
cloud server using query
transformation
3. Distributed query optimization used
to transform query process.

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Paper Publications
Zheleznya
kov.
Disadvantage:
1. Generic type of architecture used to
access different type of variable
access.
3 An
Optimistic
Approach for
Query
Construction
and
Execution in
Cloud
Computing
Environment
P.
Ravinder
Rao, S. V.
Sridhar, V.
Ramakrish
na
IJARCSS,
vol. 3, Issue
5, May 2013
Issn: 2277
128X
Cloud data
storage
Cloud
technology
Cloud data
server
Commodity
Machines
Scalable
database
Query
optimization
technique
Advantage:
1. Very large amount of cloud storage
devices has been installed.
2. Data has been accessed quickly.
3. Stored data can be accessed in an
efficient and effective way.
Disadvantage:
1. Data which is not stored in the cloud
server cannot be accessed by data
owners.
2. Data has been retrieved without any
cost of time when user given the
request at any time from any part of
the world.
4 Cloud Based
SQL Query
Processor.
Atul Phad,
Swapnil
Patil,
Sujeet
Purane,
Vineet
Patil
ISSN 2278-
4721, Vol. 2,
Issue 4
(February
2013),
Data owners
accessing
method
Data service
providers
method
Encryptional
based approach
technique
Online data
analytic tools
Outsourcing
method
Cloud server
Advantage:
1. Data has been outsourced with the
high dimensionality of services.
2. A very high range of data queries
has been executed
3. Data has been encrypted based
linear scanning devices.
4. Very rich storage of the data in
cloud environment.
Disadvantage:
1. Cloud processing is very much
expensive.
2. Very few amount of encryption
solution will be focused in the
extracting prior knowledge.
5 Rasp:
Efficient
multidimensi
onal range
query on
attack-
resilient
encrypted
databases.
K. Chen,
R.
Kavuluru,
and S. Guo
ACM
Conference
on Data and
Application
Security and
Privacy,
2011
Content
sharing and
collaborative
services
method
Spatial
transformation
technique
Cloud server
and local host
transformation
method
Enhanced HSD
Cryptographic
Transformation
(CRT)
Advantages:
1. Data owners able to share private
spatial database like tagging,
spamming.
2. Authentication and authorized user
community has been developed.
3. Very high range of query processing
has been communicated between the
local host and cloud server.
4. Confidentiality has been increased.
Disadvantage:
1. Trading the data between query
efficiency and privacy data somewhat
difficult during the networking
congestion.
6 Enabling
search
services on
outsourced
private
spatial data
M. L. Liu,
G. Ghinita,
C. S.
Jensen and
P. Kalnis
The
International
Journal of on
Very Large
Data Base,
vol. 19, no.
Structured data
management
method
Host system
management
Data
management
technique
Cloud
Advantages:
1. Newly developed data management
technique has been created.
2. Enhanced various cloud service
providers.

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Paper Publications
3, 2010 configuration
RDMS query
processing
language
environment
Cloud server and
local host
accessing tool.
3. RDMS structured language been
implemented.
4. Efficient algorithm used for the
cloud environment.
Disadvantages:
1. SQL query language cannot directly
implement.
2. Traditional database and relational
database cannot support data
management technique.
7 ESQP: An
Efficient
SQL Query
Processing
for Cloud
Data
Management
.
Jing Zhao,
Xiangmei
Hu and
Xiaofeng
Meng
ACM 978-1-
4503-0380-
4/10/10
Operational
cost method
Matching
performance
analysis
method
Scale – up and
scale down
process.
Java, Information
Tools.
Cloud
Infrastructure
tool
Software
Application
Advantages:
1. Horizontal scalability has been
accessed by the virtual
Machine.
2. Application software has been
licensed for the usage.
3. Building up a longer running cloud
services and virtual machine.
4. Performance increased.
Disadvantage:
1. Only single virtual machine
accessed through the cloud server.
8 Above the
clouds: A
berkeley
view of
cloud
computing.
M.
Armbrust,
A. Fox, R.
Griffith, A.
D. Joseph,
R. K. and
Andy
Konwinski
, G. Lee,
D.
Patterson,
A. Rabkin,
I. Stoica,
and M.
Zaharia
Technical
Report,
University of
Berkerley,
2009
Data
perturbation
method
Privacy data
mining method
Privacy
Gurantee
Translation
pertubation
Multidimensiona
l dataset
Classification
analog
Virtual machine
Advantages:
1. Data perturbation is the popular
method.
2. Accessing privacy data mining.
3. Balanced data privacy and quality
performance.
4. Multidimensional dataset has been
implemented.
5. Different types of operating system
and virtual system management have
been clubbed.
Disadvantages:
1. Geometrical analysis causes noise
addition.
9 Towards
attack-
resilient
geometric
data
perturbation
K. Chen,
L. Liu, and
G. Sun
SIAM Data
Mining
Conference,
2007
Sensitive data
type method
Standard
database value
Encrypted
value
Query
processing
method
Order –
Preserving
Encryption
Scheme (OPES)
Encrypted cloud
data.
Production
performance
tools.
Advantages:
1. Sensitive data types have been
protected.
2. Various types of numeric data has
been compared and encrypted in
cloud.
3. Query processing in the cloud
environment
4. Encrypted value services.
Disadvantage:
1. Encryption of data takes slow
process.
2. Identifying standard database takes
time.
10 Order
preserving
encryption
R.
Agrawal,
J. Kiernan,
Proceedings
of ACM
SIGMOD
Software as the
service
Database as the
Network
Topology
Advantages:
1. Internet technologies has been
implemented in cloud.

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Paper Publications
for numeric
data.
R. Srikant,
and Y. Xu
Conference,
2004
service method
Privacy Issues.
Internet
Cloud server
Cloud services
providers
Data owner
2. Software as the service imported for
the industrial needs.
3. Database as the service has been
created.
4. Retrieving the data from the cloud
server at any time.
Disadvantage:
1. Data owners cannot trust cloud
services providers all the time.
4. CONCLUSION
Cloud computing infrastructures are popularly used by peoples now a day. By using cloud users can save their cost for
query services. The proposed RASP method with range query and kNN query is mainly used to perturb the data given by
the owner and saved in cloud storage it also combines random injection, order preserving encryption and random noise
projection and also it has contains CPEL(Data Confidentiality, query Privacy, Efficient query processing, and Low in
house processing cost) criteria in it. By using the range query and kNN query user can retrieve their data’s in secured
manner and the processing time of the query is minimized. Case study is done on the related subject to improve the effect
of query.
REFERENCES
[1] Huiq i 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.
[2] H. Kllapi, D. Bilidas, I. Horrocks, Y. Ioannidis, E. Jiménez-Ruiz, E. Kharlamov, M. Koubarakis, D.
Zheleznyakov, "Distributed Query Processing on the Cloud: the Optique Point of View", In OWL Experiences and
Directions Workshop (OWLED 2013). Montpellier, France. May 26-30, 2013.
[3] P. Ravinder Rao, S. V. Sridhar, V. Ramakrishna, “An Optimistic Approach for Query Construction and Execution in
Cloud Computing Environment”, in IJARCSS, vol. 3, Issue 5, May 2013 Issn: 2277 128X
[4] Atul Phad, Swapnil Patil, Sujeet Purane, Vineet Patil,“Cloud Based SQL Query Processor”, in International Journal
Of Engineering And Science, Issn: 2278-4721, Vol. 2, Issue 4 (February 2013), Pp 01-04.
[5] K. Chen, R. Kavuluru, and S. Guo, “Rasp: Efficient multidimensional range query on attack-resilient encrypted
databases,” in ACM Conference on Data and Application Security and Privacy, 2011, pp. 249–260.
[6] M. L. Liu, G. Ghinita, C. S. Jensen and P. Kalnis, “Enabling search services on outsourced private spatial data”, The
International Journal of on Very Large Data Base, vol. 19, no. 3, 2010.
[7] Jing Zhao, Xiangmei Hu and Xiaofeng Meng, “ESQP: An Efficient SQL Query Processing for Cloud Data
Management”, in Proceedings of the second international workshop on Cloud data management. New York, NY,
USA: ACM 978-1-4503-0380-4/10/10, Pages 1-8.
[8] M. Armbrust, A. Fox, R. Griffith, A. D. Joseph, R. K. and Andy Konwinski, G. Lee, D. Patterson, A. Rabkin, I.
Stoica, and M. Zaharia, “Above the clouds: A berkeley view of cloud computing,” Technical Report, University of
Berkerley, 2009.
[9] K. Chen, L. Liu, and G. Sun, “Towards attack-resilient geometric data perturbation,” in SIAM Data Mining
Conference, 2007.
[10] R. Agrawal, J. Kiernan, R. Srikant, and Y. Xu, “Order preserving encryption for numeric data,” in Proceedings of
ACM SIGMOD Conference, 2004.

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