Secure and Private Machine Learning

这个项目旨在回答这样一个问题：当参与者为了个人利益而恶意行为时，机器如何从数据中学习？机器学习和统计被用于许多参与者有动机玩弄系统的技术中(例如，互联网广告投放、电子商务评级系统、金融信用风险、健康分析和智能公用电网)。然而，当数据被恶意参与者操纵时，人们对最先进的统计推断技术的表现知之甚少。该项目的成果旨在确保统计分析的准确性，同时保护数据隐私，为敌对领域中的安全机器学习提供理论基础。潜在的应用范围从网络安全防御到平衡安全和隐私利益的措施。

This project intends to answer the question: How can machines learn from data when participants behave maliciously for personal gain? Machine learning and statistics are used in many technologies where participants have an incentive to game the system (eg internet ad placement, e-commerce rating systems, credit risk in finance, health analytics and smart utility grids). However, little is known about how well state-of-the-art statistical inference techniques fare when data is manipulated by a malicious participant. The project's outcomes aim to ensure that statistical analysis is accurate while preserving data privacy, providing theoretical foundations of secure machine learning in adversarial domains. Potential applications range from cybersecurity defences to measures for balancing security and privacy interests.