Secure Personalization: Building Trustworthy Recommender Systems

安全个性化：构建值得信赖的推荐系统

• 批准号: 0430303
• 负责人: Robin Burke
• 金额: --
• 依托单位: DePaul University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-15 至 2008-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0430303&HistoricalAwards=false
• 关键词: Secure; Personalization; Building; Trustworthy; Recommender

The purpose of this research is to explore the vulnerabilities of recommendation and personalization systems in the face of malicious attacks, explore techniques for enhancing their robustness, and examine methods by which attacks can be recognized and possibly defeated. Most research in computer security focuses on protecting assets inside an organization's security perimeter from unauthorized access and modification. This project examines the problem of security for systems that are designed to be accessed and modified by the general public. How do we protect such a system from the legal but biased inputs of an attacker trying to subvert its functionality? The project will advance our understanding of the trustworthiness of recommender systems, now a crucial component in many areas from e-commerce and e-learning to content management systems. We will explore the spectrum of possible attacks against recommendation systems, and develop formal models characterizing these attacks and their impacts. We will investigate different metrics for assessing the robustness of recommendation algorithms including accuracy, stability and expected payoff to the attacker. In tandem with this theoretical work, we will conduct empirical investigations using data from a variety of domains. We will test a range of recommendation algorithms including user-based, item-based and model-based collaborative recommenders, and also explore hybrid recommendation by combining collaborative recommendation techniques with content-based and knowledge-based ones. Finally, informed by these results, we will consider how recommender systems can be secured, through improved algorithms but also by detecting attacks and responding appropriately. Our research will have significant implications for a variety of adaptive information systems that rely on users' input for learning user or group profiles. Many such systems have open components through which a malicious user or an automated agent can affect the overall system behavior.

本研究的目的是探讨的建议和个性化系统在面对恶意攻击的脆弱性，探索技术，提高其鲁棒性，并检查攻击可以识别和可能击败的方法。大多数计算机安全研究都集中在保护组织安全边界内的资产免受未经授权的访问和修改。这个项目研究的问题是安全的系统，旨在访问和修改的一般公众。 我们如何保护这样一个系统免受试图破坏其功能的攻击者的法律的但有偏见的输入？该项目将促进我们对推荐系统可信度的理解，推荐系统现在是从电子商务和电子学习到内容管理系统的许多领域的关键组成部分。我们将探索针对推荐系统的可能攻击的范围，并开发描述这些攻击及其影响的正式模型。 我们将研究评估推荐算法鲁棒性的不同指标，包括准确性，稳定性和对攻击者的预期回报。 与此同时，我们将使用来自各个领域的数据进行实证调查。 我们将测试一系列的推荐算法，包括基于用户的，基于项目和基于模型的协同推荐，并探索混合推荐技术相结合的协同推荐技术与基于内容和基于知识的。最后，根据这些结果，我们将考虑如何通过改进算法以及检测攻击和适当响应来保护推荐系统。我们的研究将对依赖于用户输入来学习用户或组配置文件的各种自适应信息系统产生重大影响。许多这样的系统都有开放的组件，恶意用户或自动代理可以通过这些组件影响整个系统的行为。