Jayaram Reddy A

Department of Software and Systems Engineering

School of Information Technology and Engineering

Vellore Campus

B k tripathy

G v manusha

G s mohisin

Fulltext

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

International Journal of Advanced Information Technology

Data clustering has been playing important roles in many areas like pattern recognition, image segmentation, social networks and database anonymisation. Since most of the data available in real life situation are imprecise by nature, many imprecision based data clustering algorithms are found in literature using individual imprecise models as well as their hybrids. It was observed by Krishnapuram and Keller that the possibilistic approach to the basic clustering algorithms is more efficient as the drawbacks of the basic algorithms are removed. This approach was used to develop the possibilistic versions of fuzzy, rough and rough fuzzy C-Means algorithms to develop their corresponding possibilistic versions. In this paper, we extend these algorithms further by proposing a possibilistic rough intuitionistic fuzzy C-Means algorithm (PRIFCM) and compare its efficiency with other possibilistic algorithms and the RIFCM. Experimental analysis is carried out by taking both numeric as well as the image data. Also, DB and the D indices are used for the comparison which establishes the superiority of PRIFCM. © 2015 IEEE.

2015 IEEE International Advance Computing Conference (IACC)

On PRIFCM algorithm for data clustering, image segmentation and comparative analysis

Use of published organizational data for a variety of purposes has the chance of violation of leakage of individual secret information. Though this is taken care by the organizations by removal or encryption of explicit identifiers, valuable information may still be leaked by quasi identifiers in the released data. The concept of k-anonymity was introduced and several algorithms in this direction have been proposed by different researchers [1, 2, 3, 4, 5, 6, 7, 8, 11] to handle this problem. But the notion of k-anonymity is susceptible to two types of attacks which necessitated the requirement of a better privacy preserving notion leading to the proposal of l- diversity [6]. In [14] a third phase is added to the two phase clustering-based k-anonymisation algorithm OKA [4] to achieve l-diversity. Recently, the clustering stage of the algorithm has been improved in [14] and the diversity stage algorithm is improved in [16] to come up with a fast l-diversity algorithm which deals with a single sensitive attribute in a relational table. Our main contribution in this paper is to develop an l-diversity algorithm to handle multi-sensitive attributes in databases. Also, we shall improve the adjustment stage algorithm so that it becomes more efficient. We also analyse and provide enough reasons to show that though the second and third stages of the algorithm are not necessary in most of the cases, we cannot avoid using these two stages in some cases. © 2011 IEEE.

2011 IEEE Recent Advances in Intelligent Computational Systems

A fast p-sensitive l-diversity Anonymisation algorithm

ACM Transactions on Algorithms

Achieving anonymity via clustering

ACM Transactions on Database Systems

A framework for efficient data anonymization under privacy and accuracy constraints

Proceedings of the VLDB Endowment

Privacy-preserving anonymization of set-valued data

An Efficient Algorithm for Anonymization of Set-Valued Data and Representation Using Fp-Tree

Journal	International Journal of Advanced Information Technology
Publisher	Academy and Industry Research Collaboration Center (AIRCC)
ISSN	2231-1920
Open Access	Yes