A survey of graph-modification techniques for privacy-preserving on networks

Recently, a huge amount of social networks have been made publicly available. In parallel, several definitions and methods have been proposed to protect users’ privacy when publicly releasing these data. Some of them were picked out from relational dataset anonymization techniques, which are riper than network anonymization techniques. In this paper we summarize privacy-preserving techniques, focusing on graph-modification methods which alter graph’s structure and release the entire anonymous network. These methods allow researchers and third-parties to apply all graph-mining processes on anonymous data, from local to global knowledge extraction.     

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.     

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.     

Factors affecting privacy disclosure on social network sites: an integrated model

The self-disclosure of personal information by users on social network sites (SNSs) play a vital role in the self-sustainability of online social networking service provider platforms. However, people’s levels of privacy concern increases as a direct result of unauthorized procurement and exploitation of personal information from the use of social networks which in turn discourages users from disclosing their information or encourages users to submit fake information online. After a review of the Theory of Planned Behavior (TPB) and the privacy calculus model, an integrated model is proposed to explain privacy disclosure behaviors on social network sites. Thus, the aim of this paper is to find the key factors affecting users’ self-disclosure of personal information. Using privacy calculus, the perceived benefit was combined into the Theory of Planned Behavior, and after some modifications, an integrated model was prescribed specifically for the context of social network sites. The constructs of information sensitivity and perceived benefit were redefined after reviewing the literature. Through a study on the constructs of privacy concern and self-disclosure, this article aims at reducing the levels of privacy concern, while sustaining online transactions and further stimulating the development of social network sites.     

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.     

一种保持结点可达性的高效社会网络图匿名算法

为了保护社会网络隐私信息,提出了多种社会网络图匿名化技术.图匿名化目的在于通过图修改操作来防止隐私泄露,同时保证匿名图在社会网络分析和图查询方面的数据可用性.可达性查询是一种基本图查询操作,可达性查询精度是衡量图数据可用性的一项重要指标.然而,当前研究忽略了图匿名对结点可达性的影响,导致较大的可达性信息损失.为了保持匿名图中结点的可达性,提出了可达性保持图匿名化（reachability preserving anonymization,简称RPA）算法,其基本思想是将结点进行分组并采取贪心策略进行匿名,从而减少匿名过程中的可达性信息损失.为了保证RPA算法的实用性,针对其执行效率进行优化,首先提出采用可达区间来高效地评估边添加操作所导致的匿名损失;其次,通过采用候选邻居索引,进一步加速RPA算法对每个结点的匿名过程.基于真实社会网络数据的实验结果表明了RPA算法的高执行效率,同时验证了生成匿名图在可达性查询方面的高精度.     

What about Privacy of My OSN Data?

In the arena of internet of things, everyone has the ability to share every aspect of their lives with other people. Social media is the most popular and effective medium to provide communication. Social media has gripped our lives in a dramatic way. Privacy of users data lying with the service providers needs to be preserved when published for the purpose of research as the release of sensitive personal information of an individual may pose security threats. This has become an important research area nowadays. To some extent, the concepts of anonymization that were earlier used to preserve privacy of relational microdata have been applied to preserve privacy of social networks data. Anonymizing social networks data is challenging as it is a complex structure with users connected to one another graphically and the most important is to preserve the structural properties of the graph depicting the social network relationships while applying such concepts. Recent studies based upon K-anonymity and L-diversity help to preserve privacy of online social networks data and subsequently identify attacks that arise while applying these techniques in different scenarios. K-anonymity equalizes the degree of the nodes to prevent the data from identity disclosure but it cannot preserve sensitive information and also cannot handle attacks arising due to background knowledge and homogeneity. To cope up with the drawbacks of K anonymity, L-diversity was introduced that protects the sensitive labels of the users. In this paper, a novel technique has been proposed which implements the combined features of K-anonymity and L-diversity. Our proposed approach has been validated using the data of real time social network–Twitter (most popular microblogging network). The performance of the proposed technique has been measured by the metrics, such as average path length, average change in sensitive labels, and remaining ratio of top influential users. It thus becomes evident from the results that the values of these parameters attained with the proposed technique for the anonymized graph has minimal variation to that of original structural graph. So, it is possible to retain the utility without compromising privacy while publishing social networks data. Further, the performance of the proposed technique has been discussed by calculating the information loss that addresses the concern of preserving privacy with the least variation of actual content viz info loss.     

A privacy self-assessment framework for online social networks

During our digital social life, we share terabytes of information that can potentially reveal private facts and personality traits to unexpected strangers. Despite the research efforts aiming at providing efficient solutions for the anonymization of huge databases (including networked data), in online social networks the most powerful privacy protection “weapons” are the users themselves. However, most users are not aware of the risks derived by the indiscriminate disclosure of their personal data. Moreover, even when social networking platforms allow their participants to control the privacy level of every published item, adopting a correct privacy policy is often an annoying and frustrating task and many users prefer to adopt simple but extreme strategies such as “visible-to-all” (exposing themselves to the highest risk), or “hidden-to-all” (wasting the positive social and economic potential of social networking websites). In this paper we propose a theoretical framework to i) measure the privacy risk of the users and alert them whenever their privacy is compromised and ii) help the users customize semi-automatically their privacy settings by limiting the number of manual operations. By investigating the relationship between the privacy measure and privacy preferences of real Facebook users, we show the effectiveness of our framework.     

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.     

基于随机模型检验的社交网络隐私保护研究

针对现有社交网络所提供静态隐私策略的隐私设置不够灵活且难以定量验证问题,提出一种动态隐私保护框架,将社交网络建模为离散时间马尔科夫链模型,通过设置触发条件实现用户动态隐私规约并将其转化为概率计算树逻辑公式,同时结合随机模型检验和运行时验证中的参数化与监控技术,保护社交网络发生随机故障情况下的用户动态隐私信息。在Diaspora开源社交网络上的实验结果表明,与静态隐私保护框架相比,动态隐私保护框架具有更高的安全性和灵活性,能较好满足用户的隐私保护需求。     

面向函数依赖的隐私保护研究

信息技术的发展为人们生活带来便利的同时也带来了个人隐私泄露的风险,数据匿名化是阻止隐私泄露的有效方法。然而,已有的匿名化方法主要考虑切断准标识符属性和敏感属性之间的关联,而没有考虑准标识符属性之间,以及准标识符属性和敏感属性之间存在的函数依赖关系。针对隐私保护的数据发布中存在的问题,研究数据之间存在函数依赖时,如何有效保护用户的隐私信息。首先针对数据集中存在函数依赖情况,提出(l,α)-多样性隐私保护模型;其次,为更好地实现用户隐私保护以及数据效用的增加,提出结合扰动和概化/隐匿的杂合方法实现匿名化算法。最后,实验验证了算法的有效性和效率,并对结果做了理论分析。     

A data driven anonymization system for information rich online social network graphs

In recent years, online social networks have become a part of everyday life for millions of individuals. Also, data analysts have found a fertile field for analyzing user behavior at individual and collective levels, for academic and commercial reasons. On the other hand, there are many risks for user privacy, as information a user may wish to remain private becomes evident upon analysis. However, when data is anonymized to make it safe for publication in the public domain, information is inevitably lost with respect to the original version, a significant aspect of social networks being the local neighborhood of a user and its associated data. Current anonymization techniques are good at identifying risks and minimizing them, but not so good at maintaining local contextual data which relate users in a social network. Thus, improving this aspect will have a high impact on the data utility of anonymized social networks. Also, there is a lack of systems which facilitate the work of a data analyst in anonymizing this type of data structures and performing empirical experiments in a controlled manner on different datasets. Hence, in the present work we address these issues by designing and implementing a sophisticated synthetic data generator together with an anonymization processor with strict privacy guarantees and which takes into account the local neighborhood when anonymizing. All this is done for a complex dataset which can be fitted to a real dataset in terms of data profiles and distributions. In the empirical section we perform experiments to demonstrate the scalability of the method and the improvement in terms of reduction of information loss with respect to approaches which do not consider the local neighborhood context when anonymizing.     

Using Visualization to Explore Original and Anonymized LBSN Data

We present GSUVis, a visualization tool designed to provide better understanding of location‐based social network (LBSN) data. LBSN data is one of the most important sources of information for transportation, marketing, health, and public safety. LBSN data consumers are interested in accessing and analysing data that is as complete and as accurate as possible. However, LBSN data contains sensitive information about individuals. Consequently, data anonymization is of critical importance if this data is to be made available to consumers. However, anonymization commonly reduces the utility of information available. Working with privacy experts, we designed GSUVis a visual analytic tool to help experts better understand the effects of anonymization techniques on LBSN data utility. One of GSUVis's primary goals is to make it possible for people to use LBSN data, without requiring them to gain deep knowledge about data anonymization. To inform the design of GSUVis, we interviewed privacy experts, and collected their tasks and system requirements. Based on this understanding, we designed and implemented GSUVis. It applies two anonymization algorithms for social and location trajectory data to a real‐world LBSN dataset and visualizes the data both before and after anonymization. Through feedback from domain experts, we reflect on the effectiveness of GSUVis and the impact of anonymization using visualization.     

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.     

The influence of user affect in online information disclosure

Personal consumer data is the fuel for information driven programs that may differentiate a firm from its competitors and create strategic advantages. However, a tension exists between the user’s desire to protect personal information and the needs of online businesses for consumer data that drive customer relationship and business intelligence applications. This study explores the roles of positive and negative affect on users’ trust and privacy beliefs that relate to the online disclosure of personal information. A model is tested using the responses of 301 Internet users who visited one of two commercial websites. The results indicate that positive affect has a significant effect on users’ website trust and privacy beliefs that motivate online information disclosure and this effect is more pronounced for users with high Internet security concerns. The idea that positive mood-inducing website features can motivate user behavior has the potential to guide the development of websites for effective information disclosure and data collection.     

High utility K-anonymization for social network publishing

Privacy and utility are two main desiderata of good sensitive information publishing schemes. For publishing social networks, many existing algorithms rely on \(k\) -anonymity as a criterion to guarantee privacy protection. They reduce the utility loss by first using the degree sequence to model the structural properties of the original social network and then minimizing the changes on the degree sequence caused by the anonymization process. However, the degree sequence-based graph model is simple, and it fails to capture many important graph topological properties. Consequently, the existing anonymization algorithms that rely on this simple graph model to measure utility cannot guarantee generating anonymized social networks of high utility. In this paper, we propose novel utility measurements that are based on more complex community-based graph models. We also design a general \(k\) -anonymization framework, which can be used with various utility measurements to achieve \(k\) -anonymity with small utility loss on given social networks. Finally, we conduct extensive experimental evaluation on real datasets to evaluate the effectiveness of the new utility measurements proposed. The results demonstrate that our scheme achieves significant improvement on the utility of the anonymized social networks compared with the existing anonymization algorithms. The utility losses of many social network statistics of the anonymized social networks generated by our scheme are under 1 % in most cases.     

LBS中基于移动终端的连续查询用户轨迹隐匿方法*

为减少现有LBS(基于位置的服务)机制给用户位置信息和个人隐私泄露带来的威胁,提出并实现了一个基于移动智能终端的连续查询用户运动轨迹保护方案.该方法利用移动终端来规划虚拟路径,以减少用户在连续查询中的隐私泄露,且不需要第三方服务器提供位置匿名服务,由用户自主决定何时启动位置隐匿机制.实验证明,提出的方法有效地隐匿了连续查询用户的位置及轨迹信息.     

Expectations of privacy and trust: examining the views of IT professionals

The growth of interactive online lifestyles and social networks has arguably left IT users more exposed to privacy breaches. While governments continue to revise privacy legislation, the issue of online business relationships and privacy expectations remain contentious. Indeed, fewer studies have explored the expectations of users who willingly and knowingly engage in online activities that carry privacy risks. In this study, we examine the expectations and attitudes towards online privacy of a select group of 102 IT professionals. Using a qualitative survey, we show that these users have expectations of online privacy, particularly securing and protecting information from unknown third parties. Unfortunately, these expectations may go unsatisfied with third-party monitoring enabling information disclosure. In response, users argue that enhanced technical and complementary administrative measures should be actively pursued to improve privacy outcomes. The article builds further understanding of privacy expectations and trust behaviours, while exposing the importance of technical credibility from the online organisation and user perspectives.     

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。     

Revealing the relationship between rational fatalism and the online privacy paradox

Previous research has revealed the privacy paradox, which suggests that despite concern about their online privacy, people still reveal a large amount of personal information and don’t take measures to protect personal privacy online. Using data from a national-wide survey, this study takes a psychological approach and uses the rational fatalism theory to explain the privacy paradox on the Internet and the social networking sites (SNSs). The rational fatalism theory argues that risks will become rational if the person believes he or she has no control over the outcome. Our results support the rational fatalism view. We found that people with higher levels of fatalistic belief about technologies and business are less likely to protect their privacy on the Internet in general, and the SNS in particular. Moreover, such relationship is stronger among young Internet users compared with older users.