PrIsing: Privacy-Preserving Peer Effect Estimation via Ising Model
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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.
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References · 9
- 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
- 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,571
- 2018Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learningcited by 1,099
- 2020Differentially Private Assouad, Fano, and Le Camcited by 54
- 2016Testing Ising Modelscited by 23
- 2023Private estimation algorithms for stochastic block models and mixture modelscited by 21
- 2022Differentially Private Community Detection for Stochastic Block Modelscited by 15
- 2020Privately Learning Markov Random Fieldscited by 14
- 2020Logistic-Regression with peer-group effects via inference in higher order Ising modelscited by 5
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