Empirical Investigation on Certain Anonymization Strategies for Preserving Privacy of Social Network Data

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Provided by: International Journal of Emerging Technology and Advanced Engineering (IJETAE)
Topic: Security
Format: PDF
In recent years, the rapid growth of web applications developed the need for private data to be published. Most of the social network data necessitates the data to be available for easy access and conversion of data to graph structure to re-identify sensitive labels of individuals became an impeccable issue. In this paper, the authors have made a detailed surveyed about the existing techniques that preserve the sensitive data in social network. It is observed that preserving the graph structure and label re-identification by adding some noise nodes to the graph makes significant change in degree is inferred from existing techniques. The anonymization methods for preservation of the private sensitive data based on cluster based approach and graph modification approaches are studied in detail.
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