基于博弈论研究社交网络内蠕虫的传播

社交网络内蠕虫的爆发对用户及社交网络造成了极大的威胁。将社交网络的普通用户和网络攻击者作为博弈双方,分析双方的行为策略集合及影响因素,得到收益矩阵的计算方法。基于博弈论确定用户面对信息超链接的点击概率,运行了仿真实验。实验结果表明,蠕虫伪装技术对蠕虫传播影响较大,用户安全意识程度则影响较小。当蠕虫危害度较小或信息价值度较大时,蠕虫传播速度将会加快。基于博弈论研究社交网络的蠕虫传播是可行的。     

社交媒体用户行为的时间模式隐私攻击方法

网络社交媒体用户个人隐私信息的保护具有极其重要的意义。现有的有关隐私保护的研究集中于一般关系型数据、位置和轨迹信息、社交网络关系等数据类型的隐私保护,而社交媒体数据结构的复杂性使得隐私泄露的情况变得更加隐蔽,现有针对其他类型数据的隐私泄露的判定方式和隐私保护方法难于直接用于社交媒体用户行为的时间模式分析。为了发现社交媒体复杂时间数据中存在的潜在隐私泄露问题,给出针对网络论坛用户的行为时间模式的隐私泄露挖掘方法,设计并实现了多方法、多选择的聚类攻击者模型,实验通过攻击社交媒体用户的行为模式数据集,发现了用户行为时间模式的特异性和网络论坛中广泛存在着的用户隐私泄露的问题,应当引起充分重视。     

基于社交网络的农业科技信息传播博弈模型探析

实施乡村振兴战略和推进农业农村现代化，离不开农业科技成果的推广应用。“互联网+”时代，社交网络为传播手段对农业科技信息推广十分有利。博弈论为农业科技信息竞争提供理论依据，协助农业科技信息传播主体在“市场争夺战”博弈中做出决策。因此，在社交网络环境下，分析社交网络、博弈论与农业科技信息服务的关系，基于社交网络构建农业科技信息竞争策略博弈模型、分析农业科技信息博弈均衡、收益函数等，揭示影响农业科技信息的因素和农业科技信息传播各主体间的利益关系，协助传播主体、传播受体做出最佳决策，实现社交网络下的农业科技信息传播水平的提升。     

基于边分割的社交网络敏感边保护技术

用户间的敏感关系是社交网络中用户的重要隐私信息。为了解决社交网络中用户间敏感关系泄露问题,提出一种边分割算法。首先,将已删除敏感边的简单匿名社交网络的非敏感边分割成多条子边;然后,将原非敏感边携带的信息分配到子边上,使得每条子边只携带原非敏感边的部分信息,从而生成具有隐私能力的匿名社交网络。理论分析和仿真实验结果表明,相比cluster-edge和cluster-based with constraints算法,边分割算法在保证数据具有较高可用性的情况下能更大限度降低敏感关系泄露的概率,泄露概率分别降低了约30%和20%,因此所提算法能够有效解决社交网络中敏感关系泄露问题。     

基于节点分割的社交网络属性隐私保护

现有研究表明,社交网络中用户的社交结构信息和非敏感属性信息均会增加用户隐私属性泄露的风险.针对当前社交网络隐私属性匿名算法中存在的缺乏合理模型、属性分布特征扰动大、忽视社交结构和非敏感属性对敏感属性分布的影响等弱点,提出一种基于节点分割的隐私属性匿名算法.该算法通过分割节点的属性连接和社交连接,提高了节点的匿名性,降低了用户隐私属性泄露的风险.此外,量化了社交结构信息对属性分布的影响,根据属性相关程度进行节点的属性分割,能够很好地保持属性分布特征,保证数据可用性.实验结果表明,该算法能够在保证数据可用性的同时,有效抵抗隐私属性泄露.     

基于Skyline计算的社交网络关系数据隐私保护

随着社交软件的流行，越来越多的人加入社交网络产生了大量有价值的信息，其中也包含了许多敏感隐私信息。不同的用户有不同的隐私需求，因此需要不同级别的隐私保护。社交网络中用户隐私泄露等级受社交网络图结构和用户自身威胁等级等诸多因素的影响。针对社交网络数据的个性化隐私保护问题及用户隐私泄露等级评价问题，提出基于Skyline计算的个性化差分隐私保护策略（PDPS）用以发布社交网络关系数据。首先构建用户的属性向量；接着采用基于Skyline计算的方法评定用户的隐私泄露等级，并根据该等级对用户数据集进行分割；然后应用采样机制来实现个性化差分隐私，并对整合后的数据添加噪声；最后对处理后数据进行安全性和实用性的分析并发布数据。在真实数据集上与传统的个性化差分隐私方法（PDP）对比，验证了PDPS算法的隐私保护质量和数据的可用性都优于PDP算法。     

移动社交网络中大学生用户隐私泄露防范措施探究

为了更客观对移动社交网络中大学生用户个人信息泄露现状进行分析和风险评估,本文通过对移动社交网络中用户进行聚类分析识别其中的大学生用户,利用关联规则分析大学生用户隐私泄露风险特征,从中发现用户信息泄露的关键因素,最后探析用户隐私泄露防范措施,在一定程度上提高移动社交网络中大学生用户个人隐私及个人信息保护意识,同时也为国家逐步制定和出台有关移动网络平台用户信息管理规范政策提供数据支撑。     

基于网络通讯信息安全的保障研究

随着我国信息时代的不断发展,信息技术和科学进行交融,逐渐形成信息化的经济产业,网络的通讯技术也开始广泛应用在各个行业领域,其中包括异地远程社交和网络购物。但是,网络通讯信息交流技术也存在一些漏洞,如个人信息泄露、个人隐私盗取、消费情况泄露、网络诈骗、网络绑架等,都会产生很多不良后果。基于此,本文重点探讨了网络信息安全保障方面的问题。     

面向社交网络的访问控制模型和策略研究进展

作为信息保护有效手段之一的访问控制技术,可以保障信息仅能被合法用户访问,防止信息的泄露,势必成为在线社交网络中信息保护方法的重要组成部分。通过分析在线社交网络的特点,从访问控制模型与访问控制策略2个角度出发,对目前在线社交网络中的访问控制研究进行深入探讨,并对相关研究进行整理与分析。     

基于广播加密的P2P社交网络方案的设计与实现

针对当前社交网络个人隐私信息泄露的问题,面向P2P社交网络提出一种匿名公钥广播加密方案,包括初始化公钥,分发私钥,加密和解密。并在Android平台下设计实现了基于广播加密的P2P社交网络系统,系统主要包括数据存储模块,移动终端模块,安全加密模块以及PKG管理模块。同时对Android平台下的P2P社交网络在恶意用户截获私聊攻击,恶意下载攻击,篡改攻击和重放攻击的安全性进行了分析,分析结果表明该系统安全可靠。     

动态社会网络隐私保护方法研究*

针对动态社会网络数据多重发布中用户的隐私信息泄露问题,结合攻击者基于背景知识的结构化攻击,提出了一种动态社会网络隐私保护方法。该方法首先在每次发布时采用k-同构算法把原始图有效划分为k个同构子图,并最小化匿名成本;然后对节点ID泛化,阻止节点增加或删除时攻击者结合多重发布间的关联识别用户的隐私信息。通过数据集实验证实,提出的方法有较高的匿名质量和较低的信息损失,能有效保护动态社会网络中用户的隐私。     

Disclosing too much? Situational factors affecting information disclosure in social commerce environment

The buying and selling of goods and services are no longer limited to a general website or a physical store as social networks, such as Facebook or Pinterest, are heavily focusing on social commerce. Prior studies have analyzed impact of trust and culture on social commerce, design and interface aspects of it, and intention to use social commerce by general people. Our study is informed by the literature on information disclosure intention, and Communication Privacy Management theory and is motivated by the fundamental premise that intention to self-disclose in social commerce is affected by perceived ownership of information, privacy apathy, the risks and benefits of disclosure and fairness of information exchange. We analyzed data collected from 252 samples using the scenario method. The results show that shoppers’ information disclosure intention is driven by the fairness of information exchange, privacy benefits and privacy apathy.     

Factors mediating disclosure in social network sites

In this paper, we explore how privacy settings and privacy policy consumption (reading the privacy policy) affect the relationship between privacy attitudes and disclosure behaviors. We present results from a survey completed by 122 users of Facebook regarding their information disclosure practices and their attitudes about privacy. Based on our data, we develop and evaluate a model for understanding factors that affect how privacy attitudes influence disclosure and discuss implications for social network sites. Our analysis shows that the relationship between privacy attitudes and certain types of disclosures (those furthering contact) are controlled by privacy policy consumption and privacy behaviors. This provides evidence that social network sites could help mitigate concerns about disclosure by providing transparent privacy policies and privacy controls.     

基于区块链技术架构的隐私泄露风险评估方法

区块链技术的广泛应用导致其隐私泄露问题日益严重。为有效评估区块链技术存在的隐私泄露风险,从区块链技术架构的角度,通过基于博弈的方法对攻击进行量化,提出一种区块链隐私泄露风险评估方法。考虑用户对区块链技术架构层中各攻击的敏感性程度不同,构建用户敏感度矩阵,分别计算主观敏感度与客观敏感度。利用区块链诚实用户与恶意攻击者之间的策略交互过程构建不完全信息静态贝叶斯博弈模型,通过双方的期望收益定义风险影响性与可能性,从而得到基于区块链技术架构层的风险评估分数以及整个区块链的隐私泄露风险评估分数。在此基础上,利用Sigmoid函数对风险评估分数进行正则化处理并根据风险指数判断隐私泄露的风险等级,将隐私泄露分为风险可忽略、风险适中以及风险异常3种状态。实验结果表明,该方法能够有效评估区块链隐私泄露风险情况,指导用户进行多层次、有针对性的隐私保护。     

Saving face on Facebook: privacy concerns,social benefits,and impression management

The nearly ubiquitous use of online social networks generally entails substantial personal disclosure and elicits significant privacy concerns. This research uses Social Exchange Theory and the impression management (IM) literature to examine how privacy concerns can be counterbalanced by the perceived social benefits afforded by a social network’s ability to support IM. We frame social network use as an attempt to engage in IM, and we highlight the importance of a social network’s IM affordances in predicting social benefits from, and disclosure through, a social network. We test our model with a sample of 244 Facebook users, finding support for the proposed relationships and yielding the following contributions. First, this research provides a novel positioning of perceived IM affordances as a primary driver of both perceived social benefits and IM disclosure propensity. Second, this research illuminates that trust in both the social network provider and social network peers influences privacy concerns, social benefits, and perceived IM affordances. Our theory has important implications for researchers and practitioners interested in privacy issues within social networks.     

Preventing sensitive relationships disclosure for better social media preservation

A fundamental aspect of all social networks is information sharing. It is one of the most common forms of online interaction that is tightly associated with social media preservation and information disclosure. As such, information sharing is commonly viewed as a key enabler for social media preservation tasks. In the current situation, where information sharing and inter-user communications are made instantly possible via the widespread use of ubiquitous technologies, privacy related, and particularly information disclosure issues, are the obvious, much discussed, immediate consequences of information sharing. As a result, information disclosure, especially when multimedia data come to play, is critical for appropriate social media preservation strategies that consider and respect the privacy of social network users. Social media preservation must align with privacy protection solutions and consequently must protect sensitive information that social network users would like to keep private. In this paper, we propose a new approach to implement a privacy-oriented social media preservation strategy that prevents the disclosure of sensitive information. Instead of using a preserve-all strategy, we present a framework to personalize social media preservation tasks. We then describe our proposed rule-based algorithm to evaluate information disclosure addressing mainly relationship type disclosure and using shared photos. We also provide an experimental study to investigate the efficiency and the relevance of our approach.     

隐私保护的信息熵模型及其度量方法

隐私的量化是隐私保护技术的重要支撑,信息熵作为信息的量化手段,自然可以用于解决隐私度量问题. 基于Shannon信息论的通信框架,提出了几种隐私保护信息熵模型,以解决隐私保护系统的相关度量问题,主要包括：隐私保护基本信息熵模型、含敌手攻击的隐私保护信息熵模型、带主观感受的信息熵模型和多隐私信源的隐私保护信息熵模型.在这些模型中,将信息拥有者假设为发送方,隐私谋取者假设为接收方,隐私的泄露渠道假设为通信信道;基于这样的假设,分别引入信息熵、平均互信息量、条件熵及条件互信息等来分别描述隐私保护系统信息源的隐私度量、隐私泄露度量、含背景知识的隐私度量及泄露度量;以此为基础,进一步提出了隐私保护方法的强度和敌手攻击能力的量化测评,为隐私泄露的量化风险评估提供了一种支撑;最后,针对位置隐私保护的应用场景,给出了具体的信息熵模型及隐私保护机制和攻击能力的度量及分析.所提出的模型和隐私量化方法,可以为隐私保护技术和隐私泄露风险分析与评估提供可行的理论基础.     

社交网络下的不确定图隐私保护算法

社交网络平台的快速普及使得社交网络中的个人隐私泄露问题愈发受到用户的关心,传统的数据隐私保护方法无法满足用户数量巨大、关系复杂的社交网络隐私保护需求.图修改技术是针对社交网络数据的隐私保护所提出的一系列隐私保护措施,其中不确定图是将确定图转化为概率图的一种隐私保护方法.主要研究了不确定图中边概率赋值算法,提出了基于差分隐私的不确定图边概率赋值算法,该算法具有双重隐私保障,适合社交网络隐私保护要求高的场景.同时提出了基于三元闭包的不确定图边概率分配算法,该算法在实现隐私保护的同时保持了较高的数据效用,适合简单的社交网络隐私保护场景.分析与比较表明：与（k,ε）-混淆算法相比,基于差分隐私的不确定图边概率赋值算法可以实现较高的隐私保护效果,基于三元闭包的不确定图边概率分配算法具有较高的数据效用性.最后,为了衡量网络结构的失真程度,提出了基于网络结构熵的数据效用性度量算法,该算法能够度量不确定图与原始图结构的相似程度.     

Agile systems development: Privacy theoretical lens to challenge the full information disclosure paradigm.

Software development requires intense collaboration within, across teams, and with the client. Since the popularization of agile software development in the 2000s, the virtuous effects of transparent collaboration have been emphasized. However, some studies have alluded to concerns with full information disclosure and mentioned that teams may benefit by withholding some information. We used case studies to explore such situations. We adopted a privacy theoretical lens because it is antithetical to the information disclosure lens and offers frames to comprehend how professional information can be retained by individuals, shared to restricted circles or entirely divulged. We found situations when the disclosure paradigm is challenged in agile processes. There is ambiguity in the disclosure message because individual and collective information retention do play a role in agile development. Our findings help refine information systems development philosophies and practices. We also extend our understanding of the communication privacy management theory to a context where full disclosure is preferred to privacy, and where control over privacy depends on allowances granted by a third party, which can generalize to other settings (e.g., social media platforms).     

Privacy,trust and control: Which relationships with online self-disclosure?

A number of studies have examined the relationship between privacy concerns, perceived control over information, trust and online self-disclosure, highlighting different points of view to understand this connection. This paper intends to compare these different models of explanation for self-disclosure behaviors in online social networks. Three different hypotheses are verified, using mediation and moderation analyses. The results allow underling the effect of the interaction between privacy concerns and trust on online self-disclosure, along with the absence of a direct influence of privacy concerns on disclosure itself. The results suggest practical implications for online social network providers, most of all with regard to privacy policies in online environments.