Privacy for All
, , and . 2018. Privacy for All: Ensuring Fair and Equitable Privacy Protections. In Proceedings of the 1st Conference on Fairness, Accountability and Transparency (FAT* 2018). PMLR, Proceedings of Machine Learning Research 81:35–47. Acceptance rate: 24%. Cited 87 times. Cited 67 times.
Abstract
In this position paper, we argue for applying recent research on ensuring sociotechnical systems are fair and non-discriminatory to the privacy protections those systems may provide. Privacy literature seldom considers whether a proposed privacy scheme protects all persons uniformly, irrespective of membership in protected classes or particular risk in the face of privacy failure. Just as algorithmic decision-making systems may have discriminatory outcomes even without explicit or deliberate discrimination, so also privacy regimes may disproportionately fail to protect vulnerable members of their target population, resulting in disparate impact with respect to the effectiveness of privacy protections.
We propose a research agenda that will illuminate this issue, along with related issues in the intersection of fairness and privacy, and present case studies that show how the outcomes of this research may change existing privacy and fairness research. We believe it is important to ensure that technologies and policies intended to protect the users and subjects of information systems provide such protection in an equitable fashion.
Resources
Talk
These are the papers that we cited in the talk.
- Dan Frankowski, Dan Cosley, Shilad Sen, Loren Terveen, and John Riedl. 2006. You are what you say: privacy risks of public mentions. In Proc. SIGIR ’06, 565–572.
- Arvind Narayanan and Vitaly Shmatikov. 2008. Robust De-anonymization of Large Sparse Datasets. In Proc. IEEE Symposium on Security and Privacy 2008, 111–125.
- Lorenzo Franceschi-Bicchierai. 2015. Redditor cracks anonymous data trove to pinpoint Muslim cab drivers. Mashable. Retrieved February 9, 2018 from https://mashable.com/2015/01/28/redditor-muslim-cab-drivers/.
- Michael Veale and Reuben Binns. 2017. Fairer machine learning in the real world: Mitigating discrimination without collecting sensitive data. Big Data & Society 4, 2 (November 2017).
- Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel. 2012. Fairness Through Awareness. In Proc. ITCS ’12, 214–226.