Analysing Fairness of Privacy-Utility Mobility Models

Author:

Zhan Yuting1ORCID,Haddadi Hamed1ORCID,Mashhadi Afra2ORCID

Affiliation:

1. Imperial College London, United Kingdom

2. University of Washington, United States

Publisher

ACM

Reference39 articles.

1. Aristos Aristodimou , Athos Antoniades , and Constantinos  S Pattichis . 2016. Privacy preserving data publishing of categorical data through k-anonymity and feature selection. Healthcare technology letters 3, 1 ( 2016 ), 16–21. Aristos Aristodimou, Athos Antoniades, and Constantinos S Pattichis. 2016. Privacy preserving data publishing of categorical data through k-anonymity and feature selection. Healthcare technology letters 3, 1 (2016), 16–21.

2. Solon Barocas , Moritz Hardt , and Arvind Narayanan . 2017. Fairness in machine learning. Nips tutorial 1 ( 2017 ), 2017. Solon Barocas, Moritz Hardt, and Arvind Narayanan. 2017. Fairness in machine learning. Nips tutorial 1 (2017), 2017.

3. On the apparent conflict between individual and group fairness

4. Anne Elizabeth Brown . 2018. Ridehail revolution: Ridehail travel and equity in Los Angeles . University of California , Los Angeles . Anne Elizabeth Brown. 2018. Ridehail revolution: Ridehail travel and equity in Los Angeles. University of California, Los Angeles.

5. On the Privacy Risks of Algorithmic Fairness

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