Structural Data De-anonymization: Quantification, Practice, and Implications
By S. Ji, Weiqing Li, Mudhakar Srivatsa, Raheem A. Beyah. 2014
112 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Sociology and Political Science
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Prateek Mittal
- Neil Zhenqiang Gong
- Bo Li
- Supriyo Chakraborty
- Changchang Liu
- Bowen Zhou
- Jinfeng Yi
- Shuai Di
In Electrical and Electronic Engineering
In Sociology and Political Science
- Prateek Mittal
- Bo Li
- Supriyo Chakraborty
- Neil Zhenqiang Gong
- Changchang Liu
- S. Ji
- Weiqing Li
- Raheem A. Beyah
Citations in the corpus
- Dependence Makes You Vulnberable: Differential Privacy Under Dependent Tuples (2016, methodology): “Social and behavioral dependence have also been used to perform de-anonymization attacks on released datasets [22], [32], [33], [37].”
- On Your Social Network De-anonymizablity: Quantification and Large Scale Evaluation with Seed Knowledge (2015, methodology): “6 Flixster [21] is a social movie site allowing users to share movie ratings, discover new movies and meet others with similar tastes in movies.”
- Trustworthy AI: From Principles to Practices (2021, background): “Desirable data anonymization is expected to be immune from data de-anonymization or reidentification attacks that try to recover private information from anonymized data [111, 175].”