SaTC: CORE: Small: Collaborative: Understanding and Mitigating Adversarial Manipulation of Content Curation Algorithms

SaTC：核心：小型：协作：理解和减轻内容管理算法的对抗性操纵

• 批准号: 1813697
• 负责人: Rachel Greenstadt
• 金额: $ 25万
• 依托单位: Drexel University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-15 至 2019-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1813697&HistoricalAwards=false
• 关键词: SaTC; CORE; Small; Collaborative; Understanding

Online social networks (OSNs) have fundamentally transformed how billions of people use the Internet. These users are increasingly discovering books, music bands, TV shows, movies, news articles, products, and other content through posts from trusted users that they follow. All major OSNs have deployed content curation algorithms that are designed to increase interaction and act as the "gatekeepers" of what users see. While this curation and filtering is useful and necessary given the amount of content available, it has also exposed people and platforms to manipulation attacks whereby bad actors attempt to promote content people would otherwise prefer not to see. This has driven the creation of an underground ecosystem that provides services and techniques tailored towards subverting OSNs' content curation algorithms for economic and ideological gains. This project will conduct open research to improve our understanding of current algorithmic curation attackers. The team will devise content curation algorithms and defenses which are hardened against manipulation and that can be adopted by these OSN platforms, providing a systematic approach to improving design and practice in an area of critical national importance. Technology transfer from this project will protect the integrity of social media discourse from adversarial manipulation. This project will train students with expertise in security and machine learning, areas of broad national need, and produce educational materials to engage both high school students and the public in these critical questions. The team will holistically explore the economic, social, and technical perspectives of machine learning-based content curation algorithms' weaknesses. The research comprises three main activities: 1) understand how OSNs are currently being successfully manipulated at large scales, 2) investigate the defenses OSNs have in place, and 3) design more resilient defenses. The team will build the first-ever taxonomy of manipulation services and techniques that are actively used to manipulate curation algorithms. Another thrust of the project is to create a framework for the external evaluation of deployed manipulation defenses based on the collection of both public data from the OSN's platform and external data to compare it against. The team will then develop robust and scalable algorithms to detect OSN manipulation within the collected data. Finally, the team will use the insights from the taxonomy of effective manipulation techniques and the exploration of the limitation of current defenses to design fundamentally resilient content curation algorithms. The project will explore both new curation algorithms and more effective mitigation techniques for existing algorithms. The project's findings will deepen our understanding of social network manipulation and adversarial learning and produce reliable approaches to algorithmic content curation.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.

在线社交网络（OSN）从根本上改变了数十亿人使用互联网的方式。这些用户越来越多地发现书籍，音乐乐队，电视节目，电影，新闻文章，产品和其他内容，这些帖子来自受信任的用户的帖子。所有主要的OSN都已部署了内容策划算法，这些算法旨在增加交互作用并充当用户所看到的“守门人”。鉴于可用的内容量，这种策划和过滤是有用的和必要的，但它还使人们和平台接触了操纵攻击，因此坏演员试图促进人们否则不希望看到的内容。这推动了地下生态系统的创建，该生态系统提供了为颠覆OSN的内容策划算法而定制的服务和技术。该项目将进行开放研究，以提高我们对当前算法策划攻击者的理解。该团队将设计内容策划算法和防御措施，这些算法和防御能力可以被这些OSN平台采用，这些算法可以通过这些OSN平台采用，从而在具有至关重要的国家重要性的领域提供了系统的改进设计和实践方法。来自该项目的技术转移将保护社交媒体话语的完整性免受对抗性操作。该项目将培训具有安全和机器学习专业知识，广泛国家需求领域的专业知识的学生，并生产教育材料，使高中生和公众参与这些关键问题。该团队将整体探索基于机器学习的内容策划算法的弱点的经济，社会和技术观点。该研究包括三个主要活动：1）了解当前如何在大规模上成功操纵OSN，2）研究OSN的防御能力以及3）设计更多的弹性防御能力。该团队将建立有史以来的第一个操纵服务和技术分类学分类法，这些分类和技术可积极用来操纵策展算法。该项目的另一个作用是创建一个框架，以根据从OSN平台收集公共数据和外部数据来对部署的操纵防御进行外部评估，以将其与之进行比较。然后，团队将开发可靠且可扩展的算法，以检测收集的数据中的OSN操纵。最后，团队将利用有效操纵技术的分类法中的见解，以及探索当前防御措施从根本上设计弹性内容策划算法的局限性。该项目将探索新的策展算法和现有算法更有效的缓解技术。该项目的发现将加深我们对社交网络操纵和对抗性学习的理解，并为算法内容策划提供可靠的方法。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智力优点和更广泛的影响评估标准通过评估来支持的。