PrIsing: Privacy-Preserving Peer Effect Estimation via Ising Model
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866913215098650624 |
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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 |