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Certain Investigations on Effective Query Service Mechanism in Clouds

S.Dhaarani, K.Anitha, Prof.M.Sarmila and Prof.S.Balamurugan
 Department of IT, Kalaignar Karunanidhi Institute of Technology, Coimbatore, TamilNadu, India
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International Journal of Innovative Research in Science, Engineering and Technology

Abstract

This paper details about various methods prevailing in literature for effective query service mechanisms in cloud. A privacy indexing for range query is discussed in detail. Methods for deriving privacy information from randomized data are portrayed. Geometric data perturbation for privacy preserving outsourced data mining and attackresilient geometric data perturbation private information retrieval and Dynamic authenticated index structures for outsourced databases are examined. Various attacks view of distance preserving maps for privacy preserving data mining and privacy preserving index for range queries are semantically evaluated. Various methods to derive private information from randomized data are studied in detail. This paper would promote a lot of research in the area of query service mechanism in clouds.

Keywords
Privacy Index, Data Perturbation, Dynamic Authentication, Privacy Preserving Data Mining(PPDP), Randomized Data.

INTRODUCTION
Researching on Cloud Security issues, makes to know about the use both in academic and as well as in industrial works. The Cloud can be used only through the Internet and the data stored in Cloud is very Secure and privacy so that the Authentication in Cloud by User can retrieve a Secure Transaction , in other hand the user must ensure that data received doesn’t had an any error. Wang et al who addressed cloud is a Secured and Dependable storage area. Cloud servers can have a Byzantine failure which mean any unknown error can occur in Cloud and even an Colluding attack , to prevent the error data must be encrypted while Designing the Secure storage techniques. The Searching of encrypted data is an important concern in the Cloud in which queries are used to Obtain the data. Cloud should not know the Query but it must be able to return the records by specifying the keyword through the Query,thus the records are obtained only with the exact keyword. Many researcher had addressed about the security and privacy that protects in clouds, Wang et al had introduced Reed-Solomon erasure coding which is used to check the Multiple random symbol error. The Authentication by the users are been using the public Cryptographic Technique. Many Homomorphism Encryption have been suggested that cloud couldn’t able to read the data while the cloud Computation , Using the homomorphism Encryption the cloud gets the Encrypted data by the User which mean a Cipher Text and this text is decoded and sent to the receiver and checks the cipher text received, but the Cloud cannot know what the data is been operated.

A PRIVACY PRESERVING INDEX FOR RANGE QUERIES
In this paper author introduced a privacy preserving techniques is data partitioning(bucketization). In this technique, authors built privacy preserving indices on sensitive attributes of a relational table. The author proposed two useful techniques for privacy measures. First, optimal algorithm for data partitioning which is used for maximizes the accuracy of query processing. Second, controlled diffusion algorithm is used for achieve the data privacy for data owners. In this both algorithm is used for efficient Query processing and accuracy measures. Then to achieve a data privacy from the outsides.

DERIVING PRIVATE INFORMATION FROM RANDOMIZED DATA
Data marking in privacy-preserving data mining is achieved by randomization is that the data have high risk of disclosing their. Private contents even though they are randomized. The author proposed two data construction methods. Based on data correlation. One method, principle component analysis (p(a),and other method uses the Bayes estimate (BE). Authors analysed the relationship between data correlation and amount of private information. If data correlation is high the original data must be build up (or) reorganised more accurately. The author describes more private information can be revealed. Using this method, the data privacy is decreased data correlation affects the privacy of data. To improve data privacy, we can recognize the data.

GEOMETRIC DATA PERTURBATION FOR PRIVACY PRESERVING OUTSOURCED DATA MINING
In this paper author describe the geometric data perturbation for privacy preserving outsourced data mining. These data perturbation act as major role in privacy preserving data mining because this does not guarantee to better privacy and usage. One of the earlier data mining model is multidimensional geometric information. Authors propose geometric data perturbation method, first is describes the several types of data mining model. Second it is compared with other perturbation that is random projection perturbation, rotation perturbation, translation perturbation and distance perturbation. Third perturbation against to three types of attacks naive-inference attacks, distance inference attacks. Geometric perturbation not only provide security also provide the accuracy compare to previous multidimensional perturbation techniques.

TOWARDS ATTACK-RESILIENT GEOMETRIC DATA PERTURBATION
In this paper describes the potential attacks to random geometric perturbation. Major challenges in this paper is privacy protection and data quality. In earlier many perturbation techniques such as random translation perturbation, random data perturbation are aim to provide the privacy and quality. This data perturbation is use for data owners to publish data while privacy data while privacy, we propose the framework of attacks and threats. This helps to analyze more attacks and geometric perturbation as well. They are four kinds of attack that is based upon the different level of knowledge.

PRIVATE INFORMATION RETRIEVAL
In this paper author describes the private information retrieval data base are necessary resource for retrieving data. But these provided the privacy risk to user. In this paper author gave solution to the avoid replicating database and privacy retrieval problem . They can solve the retrieval problem can obtain the following
i. A two database project with communication complexity of 0(n 1/3)
ii. A project for a constant number, M of database with communication complexity 0(n 1/m)
iii. A project for 1/3 log2 n+1 database with total communication complexity 1/3(1+0(1)).log2 n.

DYNAMIC AUTHENTICATED INDEX STRUCTURES FOR OUTSOURCED DATABASES
In this paper author describes the solution for ODB dynamic authenticated index structures. Outsourced database (ODB) is used for the database owner publishes its data through a remote servers. Query authentication is important part of ODB system. Existing solution for query authentication is a static scenarios based on the cryptographic primitives. In this work, we look at the solutions that, dynamic scenarios, in which the owners regularly update the data on serves. Finally, authors defined the query freshness, that data authentication has not been investigated before. In this paper, to improve the performance for data authentication both static and dynamic environments.

ATTACKER’S VIEW OF DISTANCE PRESERVING MAPS FOR PRIVACY PRESERVING DATA MINING
In this paper, author examines the preserving maps for privacy preserving data mining. This techniques is useful for data mining algorithm and used to transformed data. We examine how the attacker got the original information from the transformed data. The first is linear algebra and the second is principal component analysis. This techniques is used for computation efficient and good performance. It also used produce an error free result o the disrupted data. Searchable symmetric encryption(SSE) is used to store the data from one party to another party in a private manner. In this paper, author describes the solutions for SSE. SSE schemes is more efficient & secure. SSE Scheme introduces they67 two solutions. The first solution describes the design construction which avoid the pitfalls. The second solution, the queries to the server can be chosen when we call adaptive SSE security. The both solutions are very efficient and stronger security over the encrypted data. The author additionally add multi user SSE in which the data owner is rights of submitting search queries. The SSE scheme present an efficient construction & achieves better performance using access control mechanisms.

A PRIVACY PRESERVING INDEX FOR RANGE QUERIES
Transform the it operation of corporation is an emerging data management techniques. There were a privacy issues in database outsourcing. The data owners must trust the service providers is limited. In this paper author introduced a privacy preserving techniques is data partitioning(bucketization). In this techniques, authors built privacy preserving indices on sensitive attributes of a relational table. The author proposed two useful techniques for privacy measures. First, optimal algorithm for data partitioning which is used for maximizes the accuracy of query processing. Second, controlled diffusion algorithm is used for achieve the data privacy for data owners. In this both algorithm is used for efficient Query processing and Accuracy measures. .

DERIVING PRIVATE INFORMATION FROM RANDOMIZED DATA
Data marking in privacy-preserving data mining is achieved by randomization is that the data have high risk of disclosing their Private contents even though they are randomized. The author proposed two data construction methods. Based on data correlation. One method, principle component analysis (p(a)),and other method uses the Bayes estimate (BE). Authors analysed the relationship between data correlation and amount of private information. If data correlation is high the original data must be build up (or) reorganised more accurately. The author describes more private information can be revealed. Using this method, the data privacy is decreased data correlation affects the privacy of data. To improve data privacy, we can recognize the data.

CONCLUSION AND FUTURE WORK
This paper detailed about various methods prevailing in literature for effective query service mechanisms in cloud. A privacy indexing for range query is discussed in detail. Methods for deriving privacy information from randomized data are portrayed. Geometric data perturbation for privacy preserving outsourced data mining and attack-resilient geometric data perturbation private information retrieval and Dynamic authenticated index structures for outsourced databases are examined. Various attacks view of distance preserving maps for privacy preserving data mining and privacy preserving index for range queries are semantically evaluated. Various methods to derive private information from randomized data are studied in detail. This paper would promote a lot of research in the area of query service mechanism in clouds.

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