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providers. Many techniques are suggested for data protection incloud computing, but there are still a lot of challenges in thissubject. The most popular security techniques include SSL(Secure Socket Layer) Encryption, Intrusion Detection System;Multi Tenancy based Access Control, etc. Goal of this paper isto analyze and evaluate the most important security techniquesfor data protection in cloud computing. Furthermore, securitytechniques for data protection is recommended in order to haveimproved security in cloud computing.B. Privacy- Preserving Public Auditing For DataStorage Security in Cloud Computing.Author Cong Wang,Qian Wang Kui Ren have describedabout remotely storing user data on cloud and enjoy the on-demand high quality applications and services from a sharedpool of configurable computing resources, without the burdenof local data storage and maintenance in Privacy-PreservingPublic Auditing For Data Storage Security in CloudComputing7.However, the fact that users no longer havephysical possession of the outsourced data makes the dataintegrity protection in Cloud Computing a formidable task,especially for users with constrained computing resources.Also, the users should be able to use the cloud as if it werelocal, without having to worry about the need to verify itsintegrity. Thus, enabling public auditability for cloud storageis of critical importance so that users can resort to a third partyauditor (TPA) to check the integrity of outsourced data and beworry-free.The auditing process should not bring anyadditional vulnerabilities towards data privacy of users, andintroduce any additional online burden to users to securelyintroduce an effective TPA. In this paper, they authors haveproposed a secure cloud storage system supporting privacy-preserving public auditing. The authors have extended theirresult to enable the TPA to perform data audits for multipleusers simultaneously and efficiently. Extensive security andperformance analysis show the proposed schemes areprovably secure and highly efficient.C. Securing Cloud Data in Transit Using MaskingTechnique in Cloud Enabled Multi Tenant SoftwareService.Authors S. Selvakumar and M. Mohanapriya, havedescribed the issues in data security in the cloud computingenvironment in Securing Cloud Data in Transit UsingMasking Technique in Cloud Enabled Multi Tenant SoftwareService. It employs data masking to hide sensitive data fromcloud services thereby ensuring reliability and trust in thecloud environment. Data access in the cloud can becategorized into three such methods such as at rest, at transit,in use. The main aim of this paper is integrate security in datamasking techniques. We employ the existing mechanisms tothe cloud environment to secure the data with virtual machinemasking and platform masking. Findings: The masked data istransmitted to the processing environment. The services incloud utilize this masked data for processing. It iscomparatively secured when compared to the conventionaltechnique. This mechanism increases the trust worthiness andcan be masked dynamically or statistically in application ordatabase based service environments.Application/Improvements: The main application of thisresearch is to serve people with secured cloud, therebyovercoming the data security issues.D. A Few New Approaches to Data Masking.Authors G.Sarada, G.Manikandan and Dr. N.Sairamhave put forward four new approaches for masking the datausing min-max normalization, fuzzy logic, and rail-fence andmap range in A Few New Approaches to Data Masking. Fromthe experimental results it has been evident that thesetechniques overcame the limitations of the traditionalmethods. The advantage of our approach is that it makes themasked data to appear as the original data to the end users.This work can be extended in the future by using somemethods which can mask both categorical and numeric data.III. PROPOSED WORKThe user provides their login credentials and isaccordingly allowed or denied access. They can access orupdate the data as per their assigned access privileges.Whenever the data needs to be accessed for any nonproduction environments, the user will send in a querythrough the application. The application forwards this query tothe server/database. The query is processed and the result(unmasked) is captured by the application where it is maskeddynamically, after which a realistic looking but fake data isgenerated on which the tests can be carried out. This preventsthe exposure of sensitive production data to testers, developersetc.A comprehensive 4-step approach to implementing datamasking . These steps are:A. Analyse Sensitive DataThis phase identifies sensitive or regulated data across theentire organization. The purpose is to come up with the list ofsensitive data elements specific to the organization anddiscover the associated tables, columns and relationshipsacross databases that contain the sensitive data. This is carriedout usually by data, security and business analysts.B. AssessThis phase identifies the masking algorithms to replace theoriginal sensitive data. Developers or DBAs work withbusiness or security analysts with their own masking routines.C. Secure and Test:This is the iterative phase. The masking process is executedto secure the sensitive data by the security administrator. Once

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