Disparate Vulnerability: on the Unfairness of Privacy Attacks Against Machine Learning

By Mohammad Yaghini, Bogdan Kulynych, Carmela Troncoso. 2019

41 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Ahmed Salem
2. Yang Zhang
3. Lichao Sun
4. Hongsheng Hu
5. Michael Backes
6. Eugene Bagdasarian
7. Vitaly Shmatikov
8. Philip S. Yu

Citations in the corpus

• Differential Privacy Has Disparate Impact on Model Accuracy (2019, background): “Yaghini et al. [37] show that attacks exploiting this leakage disproportionately affect underrepresented groups.”
• Membership Inference Attacks on Machine Learning: A Survey (2021, background): “[26], Yaghini et al.[223], Choquette et al.”
• Updates-Leak: Data Set Inference and Reconstruction Attacks in Online Learning (2019, background): “Besides the above, there exist a wide range of other attacks and defenses on machine learning models [4,5,8,9,13,14,16, 17,22,23,25,26,35,41,43,44,46,48–50,52,54–56]”