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AGGREGATE RECOMMENDATION AND EFFECTIVE QUERY SERVICES IN THE CLOUD
WITH DATA PERTURBATION
K.Harika
1, B Dada Khalande
2, G.Rama Subba Reddy
31
M.tech Scholar (CSE),
2Asst.professor, Dept. of CSE, Vignana Bharathi Institute of Technology (VBIT),Vidya Nagar, Pallvolu, Proddatur, Kadapa(Dist), Andhra Pradesh (India)
3
Working as Associate Professor and Head of Department (CSE),
Vignana Bharathi Institute of Technology (VBIT), VidyaNagar, Pallvolu, Proddatur, Kadapa (Dist), Andhra Pradesh (India)
ABSTRACT
Sharing the data plays an important role in the cloud computing. Here there is a wide deployment of the public cloud computing, using clouds storage to host the files or data query has become a best solution for the advantage of the cost-saving. However there might be the some data which is very sensitive that the Admin does not want to move to the cloud storage unless the data is confidential and secure. On the other side, a secure query or data should provide efficient query processing that reduce the in—house workload to fully realize the benefits of the cloud computing. To avoid these here we propose the RSAP data permutation method to provide secure and efficient data range query and meanwhile KNN query services for protected data in the cloud.The RSAP data method to provide the secure and efficient preserving encryption methods, dimensionally expansions, reduces the noise injection. This KNN-R algorithm has to be designed to work with the RSAP range query algorithm to process the KNN queries. Extensive experiments have been conducted to show the advantages of this approach on efficiency and security
I. INTRODUCTION
Hosting the data query services in the cloud has been increasing rapidly because of the unique advantages in scalability and saving the cost. Cloud computing provides dynamically efficient infrastructure for the application, data and storing the file. The file transfer and file storage can be used without the interaction with the cloud service provider. Cloud computing is very cost effective because it operates at very high efficiencies with greater utilization. If we want to host the data in the cloud computing we just need an internet connection.
As the cloud computing manages load balancing that makes it more reliable. The payment that an can be pay for the cloud storage is that depends on the data usage; the infrastructure is not purchased thus the low maintenance.
These are the important features as in the cloud the working time of the query service is very high and expensive.
Here for the protection of the data and the query we need some new methods in the cloud computing. So, here we have analysed the CPEL criteria for submitting the query in the cloud. This CPEL criteria denotes
79 | P a g e confidentiality of data, query privacy and Low working cost. This method is also used to increase the complexity of the query service.
For using this query services in the cloud we use RASAP method i.e., Random Space Perturbation method. In this the query is separated as a Range query and KNN query. Here if we want to end the multiple data we use combination of order preserving encryption, random projection and random noise injection.
Data which is provided by the RASP method and its combination. This is mainly ued to protect the mutidimensionl range of queries with the secure manner and with the efficient querey processing.
Here the use of the range query is for retrieving the stored data’s. It retriews the database queries where range denotes some value between the upper bound limit to the lower boundary.
Here in the KNN query represents the K-Nearest query. K denotes the positive integer and this query is used to find the k nearest neighbour values.
II. ARCHITECTURE ANDIMPLEMENTATION DETAILS
In this architecture to store large datasets and query services cloud computing infrastructures are mainly used.
Here in this System architecture this shows in to two main parts in it. The data owner stores the huge number of data in the cloud d=n(d,k) here d denotes data, n denotes normal form of data, k denotes key value given by the data owner. This above format will be saved in the cloud in the form as d=e(d,k) here e represents perturbation.
These are separated in to two parties they are: Trusted party and the cloud service provider.
2.1 Trusted Party
In this module the trusted party will store the file or the data in the cloud. The trusted party consists of the Admin or the data owner and the authorized user. The authorized user will able to upload the data in to cloud and meanwhile by the data owner and that uploaded data will be perturbed and stored in the cloud database.
Here the authorized user needs to complete their registration first and then the user can login with his valid credentials and product key value. The product key is nothing but the admin will provide that key at the time of the registration the user will send to the authorized users email id. Thus the user can be login by giving valid credentials.
The user who is trying to login with the invalid product key will be treated as an attacker and that particular user details will be stored in the database in the attacker’s list. User will logging with valid credentials the user can perform various operations like search the content, upload the files, update the data, logout etc. Here the query
80 | P a g e search can be done for both range query and the KNN query. The authorized user can only upload the text files and that user can view the files and download the files. After competition of the task the user can logout.
The Admin will be login by giving credentials. By logging in with valid credentials the admin can view or do the various operations as he can activate or deactivate the user. Whether the user is authorized then the admin can activate the profile. If the user is un authorized then the admin can deactivate his profile. The admin can view the uploaded file that is uploaded by the user and the admin can view the attackers, file updates etc.
2.2 Cloud Service Provider
In this module the cloud service provider will store the data in the cloud storage by changing the data in to the perturbed form. Cloud service provider is a provider of web hosting services.
III. ALGORITHM (KNN-R ALGORITHM)
The authorized user generates the initial range and sends its secure perturbed form to the server.
The server will works on the secure range of queries and finds the inner range at least k points.
The server will return the points in the outer range.
The user will decrypt the points and extracts the k nearest points.
3.1 RASAP: Random Space Perturbation
In this module, we present the basic definition of the Random space perturbation (RSAP) method and its properties. Here we discuss about the attacks on RASP perturbed data, and that is defined in below section.
3.2 KNN Query Processing with RSAP
Here the RSAP perturbation does not preserve distances, KNN query cannot be directly processed with the RSAP perturbed data. In this module, we design a KNN query processing algorithm based on range queries. As a result, the use of index in range query processing also enables fast processing of KNN queries.
3.3 Manipulating User data
Here attackers cloud service providers want to gain access to their user’s data as well, mainly because of achieving profit. In addition to that hackers they have legally possibilities to access this data, mainly due to the terms of service agreement. But also more complex data and statistics are recorded, bundled and analysed (data
81 | P a g e mining) to be able to do so called user profile marketing, making prediction on what items a user might buy in the near future what is his next travel destination etc.
Here the business model of many cloud servers storages offering the free services in order to build the high personalized advertisements. They scan for the other user on tags and keywords and look for the companies which are matching these tags. This is described under the term of data mining, which means extraction, analysis and usage of data that it is stored in the cloud server. The cloud service provider can link several different kinds of user’s data to do behaviour prediction.
3.4 Leakage of Data Through Employees
Suppose if the data is stored unencrypted, then the employee of the cloud service provider may access the data and disclose it to the third party. So, it’s better to encrypt the file so that the authorised person will decrypt that file by entering the key that is provided by the admin.
3.5 Attackers
Here in this attacker’s module the main interest for the attackers in the user’s data is for illegal activities. Here the attackers attacks the user his/her confidential details from the database or the cloud server. These attackers or hacker may gain a more profit by selling this data.
IV. CONCLUSION
Here in this paper we have proposed the RSAP perturbation to host the data or the query in the cloud, which satisfies the CPEL criteria data Confidentiality, query Privacy,
Efficient query processing, and Low in-house workload.This proposed RSAP perturbation approach is specially used to perturb the data that is uploaded by the client and successfully saves that file or data in the cloud
82 | P a g e database in a secure form. This approach will allow the data confidentially, query privacy and Low working cost criteria.
Secure the data and low processing cost is a major feature to fully realize the benefits of computing effectively.
Key measure for the quality of query services is secure and efficient query processing.
In this proposed approach provides unique security features as it is a one combination
Of random noise injection, random projection and dimensionally expansion of preserving encryption (OPE).
Here the KNN_R algorithm finds the approximate KNN results so that the RSAP range query can be used. This provides the data confidential. Therefore, processing time of query is minimized to a large extent.
REFERENCES
[1] R. Agrawal, J. Kiernan, R. Srikant, and Y. Xu, “Order Preserving Encryption for Numeric Data,” Proc.
ACM SIGMOD Int’l Conf. Management of Data (SIGMOD), 2004.
[2] J. Bau and J.C. Mitchell, “Security Modeling and Analysis,” IEEE Security and Privacy, vol. 9, no. 3,pp.
18-25, May/June 2011.
[3] S. Boyd and L. Vandenberghe, Convex Optimization. Cambridge Univ. Press, 2004.
[4] N. Cao, C. Wang, M. Li, K. Ren, and W. Lou, “Privacy-Preserving Multi-Keyword Ranked Searchover Encrypted Cloud Data,” Proc. IEEE INFOCOMM, 2011.
[5] H. Hacigumus, B. Iyer, C. Li, and S. Mehrotra, “Executing SQL over Encrypted Data in the DatabaseService-Provider Model,” Proc. ACM SIGMOD Int’l Conf. Management of Data (SIGMOD), 2002.
AUTHOR DETAILS
K.Harika pursuing M.Tech (CSE) Vignana Bharathi Institute of Technology (VBIT), VidyaNagar, Pallvolu, Proddatur, Kadapa(dist),Andhra Pradesh 516 362
B Dada Khalander received his M.C.A (Computer Science &Engineering) presently he is working as Assistant Professor in Vignana Bharathi Institute of Technology, Proddutur, Kadapa Dist.A.P, INDIA
G.RamaSubbaReddy received his M.E (Computer Science &Engineering) from Sathyabama
University, Chennai.Presently he is working as Associate Professor and Head of the Department in Computer Science & Engineering, Vignana Bharathi Institute of Technology, Proddatur, KadapaDist.,A.P, INDIA
