Collaborative Research: CIF-Medium: Privacy-preserving Machine Learning on Graphs

合作研究：CIF-Medium：图上的隐私保护机器学习

• 批准号: 2402815
• 负责人: Olgica Milenkovic
• 金额: $ 40万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-05-01 至 2028-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2402815&HistoricalAwards=false
• 关键词: Collaborative; Research; CIF; Medium; Privacy

Graph-structured data captures intricate interactions between diverse agents, and is widespread in various scientific and engineering applications such as communication theory and computer science, medical research, computational biology, and social sciences. In many scenarios, graph information is sensitive and has to be kept private. Additionally, it often necessitates updates to accommodate changes in permissions, leading to the need to retrain sophisticated large-scale machine learning models from the ground up. To simultaneously ensure that the data is kept private and easily removable without complete relearning, and that its utility for making inference and predictions remains uncompromised, innovative, and efficient privacy-preserving machine learning algorithms for graph data are essential. In addition to establishing a framework for novel graph-learning method development, the project will also provide unique cross-disciplinary training opportunities for students in biological, physics, and financial graph data analysis; broaden the participation of women and other under-represented groups in STEM research via targeted recruiting and specialized student exchange programs; and, in the process, establish new collaborations among various machine learning, data acquisition and modeling centers/institutes housed at the participating institutions.This project aims to address fundamental challenges in designing privacy-preserving and efficiently updatable graph neural network models by leveraging interdisciplinary techniques from machine learning, data security, information theory, theoretical computer science and statistics. The main difficulties encountered are that (i) the graph attributes and topology are heterogeneous, yet highly correlated data types; (ii) privatization reduces utility; (iii) inference attacks that aim to determine how much information is leaking for sub-optimally privatized graph learners are generally unreliable. To resolve these issues, the team will devise novel non-uniform privatization protocols that trade accuracy for varied degrees of privacy protection; implement provably efficient methods to remove graph information from graph neural network models without retraining; and in, the process, implement a new cohort of membership inference approaches that can accurately measure information retention and leakage of machine learning models.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

图形结构的数据捕获了不同代理之间的复杂相互作用，并且在各种科学和工程应用中广泛存在，例如通信理论和计算机科学，医学研究，计算生物学和社会科学。在许多情况下，图形信息很敏感，必须保持私密。此外，它通常需要更新以适应权限的变化，从而导致需要从头开始重新训练复杂的大规模机器学习模型。同时确保数据保持私密性和易于移动而无需完整的重新学习，并且其进行推理和预测的实用性仍然毫不妥协，创新和有效的隐私机器的机器学习算法是必不可少的。除了建立新的图形学习方法开发框架外，该项目还将为生物，物理和财务图数据分析的学生提供独特的跨学科培训机会；通过有针对性的招聘和专业的学生交流计划，扩大妇女和其他代表性不足的群体在STEM研究中的参与；在此过程中，在参与机构中容纳的各种机器学习，数据获取和建模中心/机构之间建立了新的合作。该项目旨在通过从机器学习，数据安全性，信息信息，计算机科学和统计学中利用跨学科技术来应对设计具有隐私性提供和有效更新的图形神经网络模型的基本挑战。遇到的主要困难是（i）图形属性和拓扑是异质的，但高度相关的数据类型； （ii）私有化减少了效用； （iii）旨在确定对亚私有化的图表学习者泄漏多少信息的推理攻击通常是不可靠的。为了解决这些问题，团队将设计新颖的非统一私有化协议，以贸易准确性以各种隐私保护程度；实施可证明有效的方法，以从图形神经网络模型中删除图形信息，而无需重新培训；在此过程中，实施了一种新的会员推理方法，可以准确地衡量机器学习模型的信息保留和泄漏。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估评估来审查标准的评估。