AAAI 2024technical0 citations
Finding ε and δ of Traditional Disclosure Control Systems
Saswat Das, Keyu Zhu, Christine Task, Pascal Van Hentenryck, Ferdinando Fioretto
Abstract
This paper analyzes the privacy of traditional Statistical Disclosure Control (SDC) systems under a differential privacy interpretation. SDCs, such as cell suppression and swapping, promise to safeguard the confidentiality of data and are routinely adopted in data analyses with profound societal and economic impacts. Through a formal analysis and empirical evaluation of demographic data from real households in the U.S., the paper shows that widely adopted SDC systems not only induce vastly larger privacy losses than classical differential privacy mechanisms, but, they may also come at a cost of larger accuracy and fairness.
BibTeX
@article{Das_Zhu_Task_Van Hentenryck_Fioretto_2024, title={Finding ε and δ of Traditional Disclosure Control Systems}, volume={38}, url={https://ojs.aaai.org/index.php/AAAI/article/view/30204}, DOI={10.1609/aaai.v38i20.30204}, abstractNote={This paper analyzes the privacy of traditional Statistical Disclosure Control (SDC) systems under a differential privacy interpretation. SDCs, such as cell suppression and swapping, promise to safeguard the confidentiality of data and are routinely adopted in data analyses with profound societal and economic impacts. Through a formal analysis and empirical evaluation of demographic data from real households in the U.S., the paper shows that widely adopted SDC systems not only induce vastly larger privacy losses than classical differential privacy mechanisms, but, they may also come at a cost of larger accuracy and fairness.}, number={20}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Das, Saswat and Zhu, Keyu and Task, Christine and Van Hentenryck, Pascal and Fioretto, Ferdinando}, year={2024}, month={Mar.}, pages={22013-22020} }