2024-04-18-chakraborty24a.md

title	software	abstract	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
PrIsing: Privacy-Preserving Peer Effect Estimation via Ising Model	https://github.com/anirbanc96/PrIsing	The Ising model, originally developed as a spin-glass model for ferromagnetic elements, has gained popularity as a network-based model for capturing dependencies in agents’ outputs. Its increasing adoption in healthcare and the social sciences has raised privacy concerns regarding the confidentiality of agents’ responses. In this paper, we present a novel $(\varepsilon,\delta)$-differentially private algorithm specifically designed to protect the privacy of individual agents’ outcomes. Our algorithm allows for precise estimation of the natural parameter using a single network through an objective perturbation technique. Furthermore, we establish regret bounds for this algorithm and assess its performance on synthetic datasets and two real-world networks: one involving HIV status in a social network and the other concerning the political leaning of online blogs.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	chakraborty24a	0	Pr{I}sing: Privacy-Preserving Peer Effect Estimation via {I}sing Model	2692	2700	2692-2700	2692	false	Chakraborty, Abhinav and Chatterjee, Anirban and Dalal, Abhinandan	given family Abhinav Chakraborty given family Anirban Chatterjee given family Abhinandan Dalal	2024-04-18		Proceedings of The 27th International Conference on Artificial Intelligence and Statistics	238	inproceedings	date-parts 2024 4 18	https://proceedings.mlr.press/v238/chakraborty24a/chakraborty24a.pdf