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

We have too much data online in this modern world that is available to the public for some functionality or the other. This information is not misused by the general user but an adversary how wants to extract sensitive information and uses it against the user is definitely a problem in this social era. This information present online is not represented is not simple form like a tabular data but present in complex form as network graphs. The main objective of this research is to understand the problem of large graphs and provide a modified anonymized version of the original graph that holds the same structure and still anonymize to prevent information leakage. This is a huge task due to the complexity of graphs in large networks. This paper finds the problem that even though many algorithms are there for anonymity, it is still a difficult task to keep the adversary away. This work focus on partitioning the original graph based on modularity and then the edges are analysed and reconstruction of the anonymized graph takes place by modifying the edges present with the partitions such that the graph structure remains the same. The algorithm uses network hops to identify connectivity and modify the edges without losing the path. The nodes are not altered and no fake nodes or edges are added to maintain the structure. The anonymized graph can now be released and utilized. Till now work is done in edge modification and vertex modification has moved on to differential privacy leaving wide scope for the study on the problems in detail. This paper will showcase the entire graph modification with preservation of the structural properties. © BEIESP.

Computer Science

School of Computer Science and Engineering

Chennai Campus

International Journal of Innovative Technology and Exploring Engineering

Privacy preservation analysis in social network graphs for avoiding community detection and publishing sensitive information

Privacy is an important keyword in today’s social era. Information has to be shared on the social web without compromising on the sensitive information relating to a user. Major research studies have been done to generalize or suppress the sensitive information such that identity is masked or unknown to others. However, there is always a compromise in the utility of the data. Some research work is also carried on the shortest path privacy preservation which holds the sensitive data. Here the shortest path/multiple paths are masked and this data is released to the third party. In this paper, we discuss a technique with edge modification and edge swap technique on the shortest path. Since this method does not involve the deletion of edges or major link changes the utility of the released data is more. The result of this approach shows a significant increase in masking sensitive data and is also applicable to weighted undirected graphs. © 2018, Institute of Advanced Scientific Research, Inc.. All rights reserved.

Journal of Advanced Research in Dynamical and Control Systems

An approach towards privacy preservation of sensitive information in social networked graphs

In this era of 20th century, online social network like Facebook, twitter, etc. plays a very important role in everyone’s life. Social network data, regarding any individual organization can be published online at any time, in which there is a risk of information leakage of anyone’s personal data. So preserving the privacy of individual organizations and companies are needed before data is published online. Therefore the research was carried out in this area for many years and it is still going on. There have been various existing techniques that provide the solutions for preserving privacy to tabular data called as relational data and also social network data represented in graphs. Different techniques exists for tabular data but you can’t apply directly to the structured complex graph data,which consists of vertices represented as individuals and edges representing some kind of connection or relationship between the nodes. Various techniques like K-anonymity, L-diversity, and T-closeness exist to provide privacy to nodes and techniques like edge perturbation, edge randomization are there to provide privacy to edges in social graphs. Development of new techniques by Integration to exiting techniques like K-anonymity ,edge perturbation, edge randomization, L-Diversity are still going on to provide more privacy to relational data and social network data are ongoingin the best possible manner.

Fulltext

Asian Journal of Pharmaceutical and Clinical Research

A SURVEY ON PRIVACY PRESERVING TECHNIQUES FOR SOCIAL NETWORK DATA

Objectives: This paper focuses on a privacy preservation technique which is applied on graphs to preserve sensitive information present as shortest paths. Methods/Statistical Analysis: Provide privacy in Social Network Analysis by protecting the sensitive edge weights with the help of preserving the nearest shortest path lengths as well as shortest paths so that individual confidential information can be protected from multiple type of attacks. This research work provides more privacy then greedy perturbation technique in social network analysis. Findings: Privacy preservation has a tradeoffbetween the utility of data and preservation of sensitive information. This is achieved by modification of shortest path length in graphs and also maintains the structure of the graph. This procedure enhances the privacy of sensitive information with minimal concerns to utility. Application/Improvements: The privacy preservation technique of edge weight perturbation is applied to social graphs in a small user group to preserve sensitive information when data is shared within the group members. The edge weight perturbation algorithm can be improved by combing the algorithm with the preservation techniques for the user nodes.

VIT Unitversity

Indian Journal of Science and Technology

Privacy Preservation in Social Network Analysis using Edge Weight Perturbation

Nowadays it is possible to outsource data mining needs of a corporation to a third party. An establishment in the corporate world without much number or expertise in computational resources can outsource their mining needs. But the data as well as the association rules defined over it are the sole property of the company and thus privacy and security needs to be preserved. Also, partitioned databases are capable of simplifying the complexity of massive data as well as improving the overall performance of the system. In this paper we devise a scheme that ensures the privacy of data, incorporating database partitioning to ensure a highly efficient and secure system with new algorithm for privacy preservation. This is combination of L-diversity and P-sensitive technology. Agent Technology is also introduced in the given system. Different agents are used for different task like mining agent, data agent, task agent, user agent etc. and they communicate with each other and work together to provide a heuristic solution. © 2006-2015 Asian Research Publishing Network (ARPN).