Data Mining - Concepts and Techniques.

Power Aware Routing in Mobile Ad Hoc Networks

The Grid 2: Blueprint for a New Computing Infrastructure

A privacy preserving association rule mining algorithm was introducedThis algorithm preserved privacy of individual values by computing scalar productMeanwhile the algorithm of computing scalar product was given and the security was analyzed

Privacy preserving association rule mining in vertically partitioned data

The growing popularity and development of data mining technologies bring serious threat to the security of individual,'s sensitive information. An emerging research topic in data mining, known as privacy-preserving data mining (PPDM), has been extensively studied in recent years. The basic idea of PPDM is to modify the data in such a way so as to perform data mining algorithms effectively without compromising the security of sensitive information contained in the data. Current studies of PPDM mainly focus on how to reduce the privacy risk brought by data mining operations, while in fact, unwanted disclosure of sensitive information may also happen in the process of data collecting, data publishing, and information (i.e., the data mining results) delivering. In this paper, we view the privacy issues related to data mining from a wider perspective and investigate various approaches that can help to protect sensitive information. In particular, we identify four different types of users involved in data mining applications, namely, data provider, data collector, data miner, and decision maker. For each type of user, we discuss his privacy concerns and the methods that can be adopted to protect sensitive information. We briefly introduce the basics of related research topics, review state-of-the-art approaches, and present some preliminary thoughts on future research directions. Besides exploring the privacy-preserving approaches for each type of user, we also review the game theoretical approaches, which are proposed for analyzing the interactions among different users in a data mining scenario, each of whom has his own valuation on the sensitive information. By differentiating the responsibilities of different users with respect to security of sensitive information, we would like to provide some useful insights into the study of PPDM.

Information Security in Big Data: Privacy and Data Mining

Multiple alignment of protein sequences is an essential tool in molecular biology. It aids to determine evolutionary linkage and to predict molecular structures. The factors to be considered while aligning multiple sequences are speed and accuracy of alignment. Dynamic programming algorithms like Needleman-Wunsch and Smith-Waterman produce accurate alignments. But these algorithms are computation intensive and are limited to a small number of short sequences. In this paper we propose a time efficient approach to sequence alignment that produces quality alignment. The dynamic nature of the algorithm coupled with data and computational parallelism of hadoop data grids improves the accuracy and speed of sequence alignment. Further due to the scalability of hadoop framework, the proposed multiple sequence alignment is also highly suited for large scale alignment problems.

A novel approach to multiple sequence alignment using hadoop data grids

Businesses share data, outsourcing for specific business problems. Large companies stake a large part of their business on analysis of private data. Consulting firms often handle sensitive third party data as part of client projects. Organizations face great risks while sharing their data. Most of this sharing takes place with little secrecy. It also increases the legal responsibility of the parties involved in the process. So, it is crucial to reliably protect their data due to legal and customer concerns. In this paper, a review of the state-of-the-art methods for privacy preservation is presented. It also analyzes the techniques for privacy preserving association rule mining and points out their merits and demerits. Finally the challenges and directions for future research are discussed.

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A Survey on Privacy Preserving Association Rule Mining

Collaborative filtering is one of the most widely used techni ques for personalized recommendation services to users, si nce it can assist users to specify their interest on available item s. The key feature of this technique is to find similar users by applying similarity measures on user-item rating matrix. Personalized system c an thus provide recommendations for users based on the inter st of the active user as well as a likeminded users. The success of the recomme ndation process depends upon the similarity metric used to fi nd the most similar users. Similarity measures like cosine, Pearson Co rrelation Coefficient, Jaccard Uniform Operator Distance e tc are not much effective when user-item rating matrix is sparse. This pape r pr sents a new similarity model to calculate the similarit es between each user, when only few ratings are available in the user profile. The proposed model considers both global preference as well as the local context of the user behavior. Experiments are conduct e on two different datasets and compared with many existing imilarity measures. The results of the experiments show that the propo sed similarity measure improves the performance of the pers onalized recommendation process.

Modified Heuristic Similarity Measure for Personalization using Collaborative Filtering Technique

An Efficient Approach to Task Scheduling in Computational Grids.

Big data has become Buzzword in recent years. It is due to the fact that voluminous amount of structured, semi structured and unstructured data that is generated in the digital era. But, this huge data can be tracked and used for monetary benefits which thwart individual’s privacy. Hence numerous fruitful researches are made in privacy preservation. This book chapter lays emphases on the state-of-art privacy preserving data mining mechanisms and reviews the application of these mechanisms in big data environment.

G. Sudha Sadasivam

Papers

A novel approach to multiple sequence alignment using hadoop data grids

A Survey on Privacy Preserving Association Rule Mining

Modified Heuristic Similarity Measure for Personalization using Collaborative Filtering Technique

An Efficient Approach to Task Scheduling in Computational Grids.

Privacy of Big Data: A Review