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Project: Fairness in Network Anonymization


In the past 10-15 years, a massive amount of social networking data has been released publicly and analyzed to better understand complex networks and their different applications. However, ensuring the privacy of the released data has been a primary concern. Most of the graph anonymization techniques can be categorized as (i) graph modification methods and (ii) clustering-based methods. Briefly, we would like to highlight whether the graph anonymization affects the analysis for different protected groups using anonymized data is not yet studied. Besides, linkability is used to obtain useful information by mapping the data collected from different sources. This is a privacy threat, and the extent of its impact on different types of user groups in anonymized data should be analyzed to propose better fair methods.

  1. Jordi Casas-Roma, Jordi Herrera-Joancomartí, and Vicenç Torra. 2017. A survey of graph-modification techniques for privacy-preserving on networks. Artificial Intelligence Review 47, 3 (2017), 341–366

  2. Bin Zhou, Jian Pei, and WoShun Luk. 2008. A brief survey on anonymization techniques for privacy preserving publishing of social network data. ACM Sigkdd Explorations Newsletter 10, 2 (2008), 12–22.

Mykola Pechenizkiy
Secondary supervisor
Akrati Saxena
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