Data Anonymization in Social Networks State of the Art, Exposure of Shortcomings and Discussion of New Innovations

Anonymization Techniques for Privacy Preserving Data Publishing: A Comprehensive Survey

“…Structural anonymization refers to the modification in the structural properties of the social network (SN) data (i.e., graphs) to protect the privacy threats that emerge from SN data publishing. Generally, the SN analysts represent the SN data mainly via two methods: metrics and graphs [114]. The matrices representation of the SN data allow the application of computer tools and mathematical models to summarize and extract patterns.…”

“…The most common technique used for SN users privacy preservation in the PPGP is anonymization. After in-depth synthesis of the literature [114]- [116], we present the taxonomy of the PPGP approaches along with representative anonymization methods employed for SN data in Figure 10. These PPGP approaches can be broadly classified into five categories, namely graph modification techniques, graph generalization/clustering techniques, privacy aware graph computation techniques, differential privacy based graph anonymity techniques, and hybrid anonymization techniques.…”