PrIsing: Privacy-Preserving Peer Effect Estimation via Ising Model

Fuente: arXiv
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Main Authors: Chakraborty, Abhinav, Chatterjee, Anirban, Dalal, Abhinandan
Format: Preprint
Published: 2024
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author Chakraborty, Abhinav
Chatterjee, Anirban
Dalal, Abhinandan
author_facet Chakraborty, Abhinav
Chatterjee, Anirban
Dalal, Abhinandan
contents 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 $(\varepsilon,δ)$-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.
format Preprint
id arxiv_https___arxiv_org_abs_2401_16596
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle PrIsing: Privacy-Preserving Peer Effect Estimation via Ising Model
Chakraborty, Abhinav
Chatterjee, Anirban
Dalal, Abhinandan
Methodology
Cryptography and Security
Social and Information Networks
Statistics Theory
Machine Learning
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 $(\varepsilon,δ)$-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.
title PrIsing: Privacy-Preserving Peer Effect Estimation via Ising Model
topic Methodology
Cryptography and Security
Social and Information Networks
Statistics Theory
Machine Learning
url https://arxiv.org/abs/2401.16596