Data Mining deals with automatic extraction of previously unknown patterns from large amounts of data sets. These data sets typically contain sensitive individual information or critical business information, which consequently get exposed to the other parties during Data Mining activities. This creates barrier in Data Mining process. Solution to this problem is provided by Privacy preserving in data mining (PPDM). PPDM is a specialized set of Data Mining activities where techniques are evolved to protect privacy of the data, so that the knowledge discovery process can be carried out without barrier. The objective of PPDM is to protect sensitive information from leaking in the mining process along with accurate Data Mining results. The goal of this paper is to present the review on different privacy preserving techniques which are helpful in mining large amount of data with reasonable efficiency and security.
In the distributed data-intensive computing environment, securely assigning tasks to appropriate machines is a big job scheduling problem. The complexity of this problem increases with the number of jobs and their job times. Several meta-heuristic algorithms including particle swarm optimization (PSO) technique and variable neighborhood particle swarm optimization (VNPSO) technique are employed to solve the problem to a certain extent. This paper proposes a modified PSO with scout adaptation (MPSO-SA) algorithm, which uses a cyclic term called mutation operator, to solve the job scheduling problem in the cloud environment. The comparative study between the proposed MPSO-SA scheduling mechanism and the conventional scheduling algorithms show that the proposed method decreases the probability of security risk on scheduling the jobs.
Cloud Computing is huge computing, it is the internet based computing, where all users can remotely store their data into the cloud so as to enjoy the latest and high quality applications and services. In outsourcing data, users can be relieved from the burden of local maintenance and data storage .Thus, enabling public auditability for cloud data storage security is of difficult, so that users can resort to an external audit party to check the integrity of outsourced data when needed. The management of data, services may not be fully dependable when cloud moves the application software and databases to the centralized data centers and those data center is large. In this we propose a privacy-preserving public auditing for cloud data storage. To enable the TPA to perform audits for multiple users simultaneously and efficiently. And also doing batch auditing for multiple users' data.
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