PrIsing：基于Ising模型的隐私保护同伴效应估计
PrIsing: Privacy-Preserving Peer Effect Estimation via Ising Model
AI总结：
针对Ising模型在社交、医疗等领域应用引发的个体响应隐私泄露问题，提出一种基于目标扰动的(ε,δ)-差分隐私算法PrIsing，可单网络精确估计自然参数，推导了遗憾界并在合成及真实网络数据集上验证了性能。
摘要：
Ising模型最初是作为铁磁元素的自旋玻璃模型提出的，如今已成为一种常用的基于网络的模型，用于捕捉智能体输出之间的依赖关系。该模型在医疗保健和社会科学领域的应用日益广泛，引发了人们对智能体响应保密性的隐私担忧。本文提出了一种全新的(ε,δ)-差分隐私算法，专门用于保护个体智能体结果的隐私。该算法通过目标扰动技术，仅需单个网络即可精确估计自然参数。此外，我们推导了该算法的遗憾界，并在合成数据集以及两个真实世界网络上评估了其性能：一个涉及社交网络中的HIV状态数据，另一个涉及在线博客的政治倾向数据。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
