车联网中基于位置服务的个性化位置隐私保护

随着车联网的快速发展,用户享受车联网提供的位置服务(location-based services,LBSs)时,位置隐私泄漏是一个关键安全问题.针对车载网络中位置服务隐私泄露问题,提出了一种基于差分隐私的个性化位置隐私保护方案,在保护用户隐私的前提下,满足用户个性化隐私需求.首先,定义归一化的决策矩阵,描述导航推荐路线的效率和隐私效果;然后,引入多属性理论,建立效用模型,将用户的隐私偏好整合到该模型中,为用户选择效益最佳的驾驶路线;最后,考虑到用户的隐私偏好需求,以距离占比为衡量指标,为用户分配合适的隐私预算,并确定虚假位置的生成范围,以生成效用最高的服务请求位置.基于真实数据集,通过仿真实验,将所提方案与现有方案进行对比,实验结果表明:所提出的个性化位置隐私保护方案在合理保护用户隐私的情况下,能够满足用户的服务需求,以提供更高的服务质量(quality of service, QoS).     

社交网络中的位置隐私保护研究

随着智能终端设备和社交网络服务的广泛使用,移动互联网发展的一个重要趋势是社交、位置和移动相融合,在这些应用中,位置是一项非常重要的信息。该文从位置隐私泄露的风险出发,介绍了几种位置隐私保护技术,比较它们的优劣,提出了移动感知的匿位区域生成方法,通过信息熵理论将用户位置的不可推测性最大化,实现了社交网络中个人隐私保护。     

Anonymizing social networks: A generalization approach

Social networks provide a mathematical picture of various relationships that exist in society. A social network can be represented by graph data structures. These graphs are rich sources of information that must be published to share with the world. As a result, however, the privacy of users is compromised. Conventional techniques such as anonymization, randomization and masking are used to protect privacy. The techniques proposed to date do not consider the utility of published data. Absolute privacy implies zero utility, and vice versa. This paper focuses on the importance of users and the protection of their privacy. The importance of a user is determined by centrality or prestige measures. Generalization of the user is performed based on their importance to ensure privacy and utility in social networks. The information lost due to generalization is also measured.     

Protecting query privacy in location-based services

The popularity of location-based services (LBSs) leads to severe concerns on users’ privacy. With the fast growth of Internet applications such as online social networks, more user information becomes available to the attackers, which allows them to construct new contextual information. This gives rise to new challenges for user privacy protection and often requires improvements on the existing privacy-preserving methods. In this paper, we classify contextual information related to LBS query privacy and focus on two types of contexts—user profiles and query dependency: user profiles have not been deeply studied in LBS query privacy protection, while we are the first to show the impact of query dependency on users’ query privacy. More specifically, we present a general framework to enable the attackers to compute a distribution on users with respect to issuing an observed request. The framework can model attackers with different contextual information. We take user profiles and query dependency as examples to illustrate the implementation of the framework and their impact on users’ query privacy. Our framework subsequently allows us to show the insufficiency of existing query privacy metrics, e.g., k-anonymity, and propose several new metrics. In the end, we develop new generalisation algorithms to compute regions satisfying users’ privacy requirements expressed in these metrics. By experiments, our metrics and algorithms are shown to be effective and efficient for practical usage.     

半去中心化的社交网访问控制方案

提出一种将社交网网络服务与用户的数据资源管理相分离的半去中心化的社交网隐私保护方案。通过对其直接好友颁布并保存到关系服务器上的关系声明来明确用户与其好友之间的关系类型,以关系类型和访问深度作为参数定义数据资源的访问控制规则,利用关系服务器验证访问请求者与资源拥有者之间的关系路径,将关系路径与访问控制规则进行比较以决定是否授权访问。实现了依据用户指定的关系类型和深度进行授权控制,进而保护了社交网中用户数据资源的隐私。     

Agent societies and social networks for ubiquitous computing

In ubiquitous computing environments, providing appropriate services and information to users at the right place in the right way is challenging for many reasons: different user interests, heterogeneous devices and services, dynamic networks, information overload, or differing privacy levels, for example. Agent technology is a paradigm expected to play an increasing role in complex computing environments, and due to the increasing popularity of social networking services, we expect to see the convergence of agent and social web technologies. The goal of this theme issue is to bring together state-of-the-art research contributions that examine the convergence of agent technologies and social networks for ubiquitous computing.     

社交网络中用户隐私推理与保护研究综述

如今微博和Twitter等社交网络平台被广泛地用于交流、创建在线社区并进行社交活动。用户所发布的内容可以被推理出大量隐私信息,这导致社交网络中针对用户的隐私推理技术的兴起。利用用户的文本内容及在线行为等知识可以对用户进行推理攻击,社交关系推理和属性推理是对社交网络用户隐私的两种基本攻击。针对推理攻击保护机制和方法的研究也在日益增加,对隐私推理和保护技术相关的研究和文献进行了分类并总结,最后进行了探讨和展望。     

基于好友亲密度的用户匹配

用户匹配的目的是检测来自不同社交网络的用户是否是同一个人。现有的研究主要集中在用户属性和网络嵌入上,而这些研究方法往往忽略了用户与好友间的亲密关系。因此,文中提出一种基于好友亲密度的用户匹配算法(FCUM)。该算法是一种半监督、端到端的跨社交网络用户匹配算法,其中注意力机制被用于量化用户与好友之间的亲密度。好友亲密度的量化能够提高FCUM的泛化能力。通过在单一目标函数中对用户个体相似性和亲密好友相似性进行联合优化,能充分利用用户个体相似性和亲密好友相似性。文中还设计了一种双向匹配策略,用于解决人工标记匹配用户代价较高的问题。在真实数据集上的实验表明,FCUM算法优于其他只考虑用户个体相似性的方法。在如今用户隐私保护限制愈发严格、难以获取用户其他完整属性信息的情形下,该算法具有实用和易于推广的特性。     

Privacy as information access and illusory control: The case of the Facebook News Feed privacy outcry

Increasingly, millions of people, especially youth, post personal information in online social networks (OSNs). In September 2006, one of the most popular sites—Facebook.com—introduced the features of News Feed and Mini Feed, revealing no more information than before, but resulting in immediate criticism from users. To investigate the privacy controversy, we conducted a survey among 172 current Facebook users in a large US university to explore their usage behaviors and privacy attitudes toward the introduction of the controversial News Feed and Mini Feed features. We examined the degree to which users were upset by the changes, explored the reasons as to why, and examined the influences of the News Feed privacy outcry on user behavior changes. The results have demonstrated how an easier information access and an “illusory” loss of control prompted by the introduction of News Feed features, triggered users’ privacy concerns. In addition to enhancing our theoretical understanding of privacy issues in the online social networks, this research is also potentially useful to privacy advocates, regulatory bodies, service providers, and marketers to help shape or justify their decisions concerning the online social networks.     

An energy efficient privacy-preserving content sharing scheme in mobile social networks

The rising popularity of mobile social media enables personalization of various content sharing and subscribing services. These two types of services entail serious privacy concerns not only to the confidentiality of shared content, but also to the privacy of end users such as their identities, interests and social relationships. Previous works established on the attribute-based encryption (ABE) can provide fine-grained access control of content. However, practical privacy-preserving content sharing in mobile social networks either incurs great risk of information leaking to unauthorized third parties or suffers from high energy consumption for decrypting privacy-preserving content. Motivated by these issues, this paper proposes a publish–subscribe system with secure proxy decryption (PSSPD) in mobile social networks. First, an effective self-contained privacy-preserving access control method is introduced to protect the confidentiality of the content and the credentials of users. This method is based on ciphertext-policy ABE and public-key encryption with keyword search. After that, a secure proxy decryption mechanism is proposed to reduce the heavy burdens of energy consumption on performing ciphertext decryption at end users. The experimental results demonstrate the efficiency and privacy preservation effectiveness of PSSPD.