# PrIsing: Privacy-Preserving Peer Effect Estimation via Ising Model

arXiv: https://arxiv.org/abs/2401.16596 · HTML: https://mlsanity.com/paper/2401.16596/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 59 references that are in the map.
- First posted on arXiv 29 Jan 2024.

## Builds on (most cited first, 9 of 9)

- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning](https://mlsanity.com/paper/1801.07606/) (2018)
- [Differentially Private Assouad, Fano, and Le Cam](https://mlsanity.com/paper/2004.06830/) (2020)
- [Testing Ising Models](https://mlsanity.com/paper/1612.03147/) (2016)
- [Private estimation algorithms for stochastic block models and mixture models](https://mlsanity.com/paper/2301.04822/) (2023)
- [Differentially Private Community Detection for Stochastic Block Models](https://mlsanity.com/paper/2202.00636/) (2022)
- [Privately Learning Markov Random Fields](https://mlsanity.com/paper/2002.09463/) (2020)
- [Logistic-Regression with peer-group effects via inference in higher order Ising models](https://mlsanity.com/paper/2003.08259/) (2020)

## Abstract (arXiv)

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.