Collaborative Research: SaTC: CORE: Small: Securing Recommender Systems against Data Poisoning Attacks

协作研究：SaTC：核心：小型：保护推荐系统免受数据中毒攻击

• 批准号: 2125958
• 负责人: Bin Liu
• 金额: $ 10万
• 依托单位: West Virginia University Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2125958&HistoricalAwards=false
• 关键词: Collaborative; Research; SaTC; CORE; Small

The goal of this project is to build secure recommender systems against data poisoning attacks. Recommender systems are common online, suggesting movies, products, news, and many other kinds of items in order to help people find things they are interested in and make decisions. The influence recommender systems have on people's behavior, however, makes them attractive targets: attackers can create fake users who rate items in ways that lead the system to recommend products that are more in the attackers' interests than the users'. These "data poisoning" attacks threaten the integrity of recommender systems, harming both the companies and people that use them. This proposal will develop methods to detect, limit, and recover from the damage of data poisoning attacks, making recommender systems more resistant to manipulation by bad actors and thus more trustable and useful; the methods will also be incorporated into students' coursework and research work, training a next generation of computer scientists to build more robust machine learning systems.The project is structured around three main aims. Task 1 involves systematically investigating the security vulnerabilities of recommender systems against data poisoning attacks where attackers have varying levels of knowledge about the algorithms and datasets. In task 2 the team will develop new recommendation algorithms that provably prevent data poisoning attacks, i.e., a bounded number of fake users provably cannot affect the system's performance no matter how the fake users craft their rating scores. Task 3 is to develop methods to detect the fake users in data poisoning attacks with provable guarantees and efficiently recover a recommender system from data poisoning attacks. The project will provide research opportunities for students with backgrounds that are traditionally underrepresented in computing, and the work will be incorporated into courses at Duke University and West Virginia University and disseminated widely.This project is jointly funded by Secure and Trustworthy Computing (SaTC) and the Established Program to Stimulate Competitive Research (EPSCoR).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.

该项目的目的是建立针对数据中毒攻击的安全建议系统。 推荐系统在线很常见，建议电影，产品，新闻和许多其他类型的物品，以帮助人们找到他们感兴趣的东西并做出决定。 但是，推荐系统对人们行为的影响使它们成为有吸引力的目标：攻击者可以创建虚假的用户，这些用户会以导致系统推荐攻击者利益的产品的方式对项目进行评分，而不是用户。 这些“数据中毒”攻击威胁着推荐系统的完整性，损害了使用它们的公司和人员。 该提案将开发出检测，限制和从数据中毒攻击损害中恢复的方法，从而使推荐系统对不良演员的操纵更具抵抗力，从而更加可信赖和有用；这些方法还将被纳入学生的课程和研究工作中，培训下一代计算机科学家以建立更强大的机器学习系统。该项目围绕三个主要目标进行了结构。 任务1涉及系统地研究推荐系统的安全漏洞，以防止攻击者对算法和数据集具有不同水平的数据中毒攻击。在任务2中，团队将开发新的推荐算法，这些算法可防止数据中毒攻击，即，无论伪造用户如何制作其评级分数，证明有界数的伪造用户都不会影响系统的性能。任务3是开发使用可证明的保证的数据中毒攻击中的假用户的方法，并有效地从数据中毒攻击中恢复了推荐系统。该项目将为具有传统背景的学生提供研究机会，这些背景在计算方面不足，并且该工作将被纳入杜克大学和西弗吉尼亚大学的课程中，并广泛进行分散。该项目由安全且可信赖的计算（SATC）共同资助，并通过既定的计划（EPSCOR）进行了支持，并促进了统计的奖励（EPSCOR）。基金会的智力优点和更广泛的影响评论标准。

项目成果

Contrastive Learning of Temporal Distinctiveness for Survival Analysis in Electronic Health Records

• DOI: 10.1145/3583780.3614824
• 发表时间: 2023-01-01
• 期刊: PROCEEDINGS OF THE 32ND ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, CIKM 2023
• 作者: Kerdabadi, Mohsen Nayebi;Moghaddam, Arya Hadizadeh;Yao, Zijun
• 通讯作者: Yao, Zijun