Sureshkumar N

Department of Software Systems

School of Computer Science and Engineering

Vellore Campus

Sreedhar K.C

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

An optimal cloud-based e-healthcare system using k-centroid MVS clustering scheme

Journal of Intelligent & Fuzzy Systems

Cloud computing plays a predominant role in storage technologies. It enables the tenant user to deploy their infrastructure without any investment. Cloud storage offers flexibility with storage and sharing facilities using the Internet platform. Storing sensitive information such as clinical data requires high privacy preservation and is associated with serious concern over data privacy on the cloud platform. Privacy preservation becomes the most adherent issue when a large volume of data is stored in public clouds. Subtree anonymization using the bottom-up generalization (BUG) and top-down specialization (TDS) approaches has been widely adopted for anonymizing data sets. This ensures individual data privacy; however, it causes potential violations when the new update is received, and it suffers from valuing the k-anonymity parameter. In this proposed model, a pseudo-identity was anticipated to accomplish privacy preservation with maximum data utility on incremental data sets. Initially, the Data Set (DS) was partitioned in the preprocessing stage; subsequently, the processed data sets were clustered into groups. The genetic model was used for indexing and updating incremental data sets. This was consistent with repeatedly modified data sets. In the evaluation process, an incremental and distributed DS was deployed, and our model exhibited efficient and optimal performance for privacy preservation in comparison with existing models. © 2017-IOS Press and the authors. All rights reserved.

A genetic TDS and BUG with pseudo-identifier for privacy preservation over incremental data sets

Neurocomputing

Efficient privacy-preserving content recommendation for online social communities

Journal of Systems and Software

Understanding cloud computing adoption issues: A Delphi study approach

Journal of Network and Computer Applications

An efficient and secure privacy-preserving approach for outsourced data of resource constrained mobile devices in cloud computing

Procedia Computer Science

Cluster Based Outlier Detection Algorithm for Healthcare Data

Journal	Journal of Intelligent & Fuzzy Systems
Publisher	IOS Press
ISSN	1064-1246
Open Access	0