基于GAN的社交网络隐私保护方法

针对数据供应商发布社交网络数据时可能出现的泄露隐私问题，提出一种基于生成对抗网络的隐私保护方法(GPGAN)。采用GAN作为学习模型捕捉网络结构的随机游走，设计奖励函数指引创建包含重要信息的随机游走。提出基于游走样本的匿名图构造方法，通过添加差分隐私得到匿名概率邻接矩阵，重构社交网络图。实验结果表明，与其它图生成相比，该模型具有良好的图结构特征学习能力。通过度量评估实验验证了GPGAN可以在合理的隐私预算下保留所需的数据效用，优于当前主流的社交网络隐私保护方法。     

一种基于差分隐私的频繁有权子图挖掘算法

目前,在保护频繁子图数据的研究领域中,关于保护带有边权重的子图数据还没有被研究.针对这一问题,在频繁有权子图的挖掘过程中,采用差分隐私技术兼顾地保护频繁子图的边权重和结构的隐私,提出Diff-Wfsm算法.通过扩展已有挖掘算法,将图模型转换成编码形式,并将权重值考虑到编码中.为了更好地保护结构的隐私和提高数据效用性,在挖掘过程中同时采用差分隐私的Laplace机制和指数机制.实验在多个真实数据集中进行,结果表明该算法能在挖掘过程中达到隐私保护的效果,并可以保证输出的频繁有权子图具有较高的数据效用性.     

基于谱聚类的社交网络差分隐私保护算法研究

针对权重社交网络差分隐私保护算法中噪声添加量过大以及隐私保护不均衡问题,提出了一种结合谱聚类算法与差分隐私保护模型的隐私保护算法SCDP。首先针对传统差分隐私保护算法直接向社交网络边权重添加噪声方式带来的噪声添加量过大的问题,结合谱聚类算法,将权重社交网络聚类成为不同的簇,对不同的簇采取随机添加噪声的方式,降低噪声的添加量,提高数据的可用性;其次设计新的隐私预算参数,根据社交网络边权重的大小决定噪声的添加量,实现更均衡的隐私保护;最后通过理论推导和实验证明了SCDP算法处理后的数据可用性更高。     

权重社交网络隐私保护中的差分隐私算法

针对社交网络的边权重隐私泄露问题,提出一种权重社交网络隐私保护算法。利用无向有权图表示社交网络,把边权重序列作为一个无归属直方图处理,将包含敏感信息的权重加入拉普拉斯噪声以满足差分隐私保护要求。为减少噪音量,对直方图中具有相同计数的桶合并成组,根据组间k-不可区分性来保证差分隐私保护要求,通过对原始的权重序列进行一致性推理保持网络最短路径不变。理论分析和实验结果表明,该算法能够满足差分隐私保护要求,且提高了信息发布的准确性和实用性。     

基于SOM网络的差分隐私保护研究

差分隐私模型具有强大的隐私保护能力,但是也存在数据效用低等问题。为提高数据可用性并保护数据隐私,提出一种基于SOM网络的差分隐私算法(SOMDP)。首先利用SOM网络模型对数据进行聚类操作;其次,对每个划分好的聚类添加满足差分隐私的拉普拉斯噪声;最后,理论分析算法的可行性,并在真实数据集上评估SOMDP算法性能、算法的数据可用性和隐私性能。实验结果表明,SOMDP在达到差分隐私要求的前提下,可较大程度地提高差分隐私数据发布的效用。     

社交网络隐私保护中的随机算法

提出了一种基于高斯随机乘法的社交网络隐私保护方法.该算法利用无向有权图表示社交网络,通过高斯随机乘法来扰乱其边的权重,保持网络最短路径不变并使其长度应与初始网络的路径长度尽可能接近,以实现对社交网络的隐私保护.从理论上证明了算法的可行性及完美算法的不存在性.采用这种随机乘法得到的仿真结果符合理论分析结果.     

一种满足差分隐私的图赌博机算法

图赌博机是一种重要的不确定性环境下的序列决策模型, 在社交网络、电子商务和推荐系统等领域都得到了广泛的应用. 目前, 针对图赌博机的工作都只关注如何快速识别最优摇臂从而最小化累积遗憾, 而忽略了在很多应用场景中存在的隐私保护问题. 为了克服现有图赌博机算法的缺陷, 提出了一种满足差分隐私的图赌博机算法GAP (图反馈下的差分隐私摇臂消除策略). 一方面, GAP算法阶段性地根据摇臂的经验平均奖赏更新摇臂选取策略, 并在计算摇臂的经验平均奖赏时引入拉普拉斯噪声, 从而确保恶意攻击者难以根据算法输出推算摇臂奖赏数据, 保护了隐私. 另一方面, GAP算法在每个阶段根据精心构造的反馈图的独立集探索摇臂集合, 有效地利用了图形式的反馈信息. 证明了GAP算法满足差分隐私性质, 具有与理论下界相匹配的遗憾界. 在仿真数据集上的实验结果表明: GAP算法在有效保护隐私的同时取得了与现有无隐私保护的图赌博机算法相当的累积遗憾.     

加权社交网络敏感边的差分隐私保护研究

社交网络边权重表示节点属性相似性时,针对边权重能导致节点敏感属性泄露的问题,提出一种利用差分隐私保护模型的扰动策略进行边权重保护。首先根据社交网络构建属性相似图和非属性相似图,同时建立差分隐私保护算法;然后对属性相似图及非属性相似图边权重进行扰动时,设计扰动方案,并按扰动方案对属性相似图及非属性相似图进行扰动。实现了攻击者无法根据扰动后边权重判断节点属性相似性,从而防止节点敏感属性泄漏,而且该方法能够抵御攻击者拥有最大背景知识的攻击。从理论上证明了算法的可行性,并通过实验验证了算法的可行性及有效性。     

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。     

针对社交网络边权重的差分隐私保护

针对社交网络边权重隐私保护中的弱保护和最短路径不可分析问题,提出一种满足差分隐私保护模型的边权重保护策略。将社交网络划分为全次图、缺次图、零次图,设计扰动方案及查询函数,对不同图进行查询获取其边权重并按扰动方案对不同的边权重添加不同的Laplace噪声,实现抵御攻击者拥有最大背景知识的攻击的边权重隐私保护,保证一组节点的最短路径不变,且其长度与原路径长度相近。该策略有强保护性及最短路径可分析性,从理论上验证了算法的可行性,通过实验验证了算法的正确性。     

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.     

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。     

Upper approximation based privacy preserving in online social networks

With the advent of the online social network and advancement of technology, people get connected and interact on social network. To better understand the behavior of users on social network, we need to mine the interactions of users and their demographic data. Companies with less or no expertise in mining would need to share this data with the companies of expertise for mining purposes. The major challenge in sharing the social network data is maintaining the individual privacy on social network while retaining the implicit knowledge embedded in the social network. Thus, there is a need of anonymizing the social network data before sharing it to the third-party.The current study proposes to use upper approximation concept of rough sets for developing a solution for privacy preserving social network graph publishing. The proposed algorithm is capable of preserving the privacy of graph structure while simultaneously maintaining the utility or value that can be generated from the graph structure. The proposed algorithm is validated by showing its effectiveness on several graph mining tasks like clustering, classification, and PageRank computation. The set of experiments were conducted on four standard datasets, and the results of the study suggest that the proposed algorithm would maintain the both the privacy of individuals and the accuracy of the graph mining tasks.     

Stochastic graph as a model for social networks

Social networks are usually modeled and represented as deterministic graphs with a set of nodes as users and edges as connection between users of networks. Due to the uncertain and dynamic nature of user behavior and human activities in social networks, their structural and behavioral parameters are time varying parameters and for this reason using deterministic graphs for modeling and analysis of behavior of users may not be appropriate. In this paper, we propose that stochastic graphs, in which weights associated with edges are random variables, may be a better candidate as a graph model for social network analysis. Thus, we first propose generalization of some network measures for stochastic graphs and then propose six learning automata based algorithms for calculating these measures under the situation that the probability distribution functions of the edge weights of the graph are unknown. Simulations on different synthetic stochastic graphs for calculating the network measures using the proposed algorithms show that in order to obtain good estimates for the network measures, the required number of samples taken from edges of the graph is significantly lower than that of standard sampling method aims to analysis of human behavior in online social networks.     

Anonymizing popularity in online social networks with full utility

With the rapid growth of social network applications, more and more people are participating in social networks. Privacy protection in online social networks becomes an important issue. The illegal disclosure or improper use of users’ private information will lead to unaccepted or unexpected consequences in people’s lives. In this paper, we concern on authentic popularity disclosure in online social networks. To protect users’ privacy, the social networks need to be anonymized. However, existing anonymization algorithms on social networks may lead to nontrivial utility loss. The reason is that the anonymization process has changed the social network’s structure. The social network’s utility, such as retrieving data files, reading data files, and sharing data files among different users, has decreased. Therefore, it is a challenge to develop an effective anonymization algorithm to protect the privacy of user’s authentic popularity in online social networks without decreasing their utility. In this paper, we first design a hierarchical authorization and capability delegation (HACD) model. Based on this model, we propose a novel utility-based popularity anonymization (UPA) scheme, which integrates proxy re-encryption with keyword search techniques, to tackle this issue. We demonstrate that the proposed scheme can not only protect the users’ authentic popularity privacy, but also keep the full utility of the social network. Extensive experiments on large real-world online social networks confirm the efficacy and efficiency of our scheme.     

社会网络数据发布隐私保护技术综述

对社会网络隐私保护的研究现状与进展进行了阐述.首先介绍了社会网络隐私保护问题的研究背景,进而从社会网络中的隐私、攻击者背景知识、社会网络数据隐私保护技术、数据可用性与实验测评等方面对当前研究工作进行了细致的分类归纳和分析,指出了当前社会网络隐私保护的不足以及不同隐私保护技术间的对比和优缺点,并对未来需要深入研究的方向进行了展望.对社会网络数据隐私保护研究的主流方法和前沿进展进行了概括、比较和分析.     

一种动态的移动社交网络拓扑模型

针对移动社交网络的动态性、用户不同重要性和信息交互有向性,基于4种初始网络提出能准确描述移动社交网络结构的拓扑模型。采用随机游走理论和改进的PageRank算法,引入过渡概率使每两时步之间的网络拓扑结构相互联系。通过PageRank算法得到节点的势,进而求出概率过渡矩阵,利用随机游走理论由上一时步边存在概率矩阵和概率过渡矩阵得到当前时步边存在概率矩阵,每一时步动态地增加一个节点并检验是否有离开的节点。仿真结果显示,该模型在4种初始网络下得到的网络拓扑结构,入度、出度、势分布以及度-势相关性均具有明显幂律特性,表明随机游走理论和改进的PageRank算法能较准确描述移动社交网络,具有一定的实践意义。     

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.