Privacy Preservation Analysis in Social Network Graphs for avoiding Community Detection and Publishing Sensitive Information
Sharath Kumar J1, Maheswari N2
1Sharath Kumar J, Department of Computing Science and Engineering, VIT University Chennai (Tamil Nadu), India.
2Maheswari N, Department of Computing Science and Engineering, VIT University Chennai (Tamil Nadu), India.
Manuscript received on 07 April 2019 | Revised Manuscript received on 20 April 2019 | Manuscript published on 30 April 2019 | PP: 888-893 | Volume-8 Issue-6, April 2019 | Retrieval Number: F3681048619/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keyword: Social Networks, Data Anonymization, Edge Modification, Un-weighted graphs, Privacy, Social Network Randomization, Privacy Preservation Data Mining.
Scope of the Article: Computer Network