2024-09-12-cai24a.md

title	abstract	openreview	section	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Privacy-Aware Randomized Quantization via Linear Programming	Differential privacy mechanisms such as the Gaussian or Laplace mechanism have been widely used in data analytics for preserving individual privacy. However, they are mostly designed for continuous outputs and are unsuitable for scenarios where discrete values are necessary. Although various quantization mechanisms were proposed recently to generate discrete outputs under differential privacy, the outcomes are either biased or have an inferior accuracy-privacy trade-off. In this paper, we propose a family of quantization mechanisms that is unbiased and differentially private. It has a high degree of freedom and we show that some existing mechanisms can be considered as special cases of ours. To find the optimal mechanism, we formulate a linear optimization that can be solved efficiently using linear programming tools. Experiments show that our proposed mechanism can attain a better privacy-accuracy trade-off compared to baselines.	vWsf4L7rHq	Papers	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	cai24a	0	Privacy-Aware Randomized Quantization via Linear Programming	499	516	499-516	499	false	Cai, Zhongteng and Zhang, Xueru and Khalili, Mohammad Mahdi	given family Zhongteng Cai given family Xueru Zhang given family Mohammad Mahdi Khalili	2024-09-12		Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence	244	inproceedings	date-parts 2024 9 12	https://raw.githubusercontent.com/mlresearch/v244/main/assets/cai24a/cai24a.pdf