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Vulnerabilities and Countermeasures in Context-Aware Social Rating Services

机译：上下文感知的社会评级服务中的漏洞和对策

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摘要

Social trust and recommendation services are the most popular social rating systems today for service providers to learn about the social opinion or popularity of a product, item, or service, such as a book on Amazon, a seller on eBay, a story on Digg or a movie on Netflix. Such social rating systems are very convenient and offer alternative learning environments for decision makers, but they open the door for attackers to manipulate the social rating systems by selfishly promoting or maliciously demoting certain items. Although a fair amount of effort has been made to understand various risks and possible defense mechanisms to counter such attacks, most of the existing work to date has been devoted to studying specific types of attacks and their countermeasures. In this article, we argue that vulnerabilities in social rating systems and their countermeasures should be examined and analyzed in a systematic manner. We first give an overview of the common vulnerabilities and attacks observed in some popular social rating services. Next, we describe three types of attack strategies in two types of social rating systems, including a comprehensive theoretical analysis of their attack effectiveness and attack costs. Three context-aware countermeasures are then presented: (i) hiding user-item relationships, (ii) using confidence weight to distinguish popular and unpopular items, and (iii) incorporating time windows in trust establishment. We also provide an in-depth discussion on how these countermeasures can be used effectively to improve the robustness and trustworthiness of the social rating services.
机译：社会信任和推荐服务是当今最流行的社会评估系统，服务提供商可以了解产品，商品或服务的社会观点或受欢迎程度，例如亚马逊上的书，eBay上的卖家，Digg上的故事或Netflix上的电影。这样的社会评级系统非常方便，为决策者提供了替代的学习环境，但是它们为攻击者通过自私地促销或恶意降级某些项目打开了操纵社会评级系统的大门。尽管已做出大量努力来理解各种风险和可能的防御机制以应对此类攻击，但迄今为止，大多数现有工作已致力于研究特定类型的攻击及其对策。在本文中，我们认为应该系统地检查和分析社会评级系统中的漏洞及其对策。我们首先概述一些流行的社会评分服务中常见的漏洞和攻击。接下来，我们在两种类型的社会评分系统中描述三种攻击策略，包括对其攻击效果和攻击成本的全面理论分析。然后提出了三种情境感知对策：（i）隐藏用户-项目关系，（ii）使用置信度权重区分受欢迎和不受欢迎的项目，以及（iii）将时间窗口纳入信任建立。我们还深入讨论了如何有效使用这些对策来提高社会评级服务的可靠性和可信赖性。

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