基于属性相关性划分的多敏感属性隐私保护方法

近年来,基于l-多样性的多维敏感属性的隐私保护研究日趋增多,然而大部分多敏感属性隐私保护方法都是基于有损分解的思想,破坏了数据间的关系,降低了数据效用.为此,提出了一种面向多敏感属性的隐私模型,首先给出一种l-maximum原则用以满足多敏感属性l-多样性要求;其次,为了保护属性间的相关性,根据属性间的依赖度对属性进行划分;最后设计并实现了MSA l-maximum（Multiple Sensitive Attributes l-maximum）算法.实验结果表明,提出的模型在保护隐私不泄露的同时,减少了元组的隐匿率,并且保护了数据间的关系.     

融合微聚集隐私保护的协同过滤算法研究

现有的k-匿名隐私保护是一种安全有效的隐私保护算法,针对其对背景知识攻击和同质性攻击防范的不足,提出一种基于敏感属性多样性的微聚集隐私保护的协同过滤算法。算法在满足k-匿名的前提下,融入敏感属性的多样性,在微聚集算法中通过设置同一等价类中敏感属性的差异值,来避免敏感属性值过于接近而造成隐私泄露,从而达到保护隐私数据的目的,同时保证推荐的准确性。实验结果表明,该算法既能保证为用户提供高效的个性化推荐,又能够产生安全的信息表。     

一种基于逆聚类的个性化隐私匿名方法

针对不同个体对隐私保护的不同需求,提出了一种面向个体的个性化扩展l-多样性隐私匿名模型.该模型在传统l-多样性的基础上,定义了扩展的l-多样性原则,并通过设置敏感属性的保护属性来实现个体与敏感值之间关联关系的个性化保护需求.同时,还提出了一种个性化扩展l-多样性逆聚类(PELI-clustering)算法来实现该隐私匿名模型.实验表明:该算法不仅能产生与传统基于聚类的l-多样性算法近似的信息损失量以及更小的时间代价,同时也满足了个性化服务的需求,获得更有效的隐私保护.     

面向DaaS应用的数据集成隐私保护机制研究

云计算的出现为多个数据拥有者进行数据集成发布及协同数据挖掘提供了更广阔的平台,在数据即服务模式(Daa S,data as a service)下,集成数据被部署在非完全可信的服务运营商平台上,数据隐私保护成为制约该模式应用和推广的挑战性问题。为防止数据集成时的隐私泄露,提出一种面向Daa S应用的两级隐私保护机制。该隐私保护机制独立于具体的应用,将数据属性切分到不同的数据分块中,并通过混淆数据确保数据在各个分块中均衡分布,实现对数据集成隐私保护。通过分析证明该隐私保护机制的合理性,并通过实验验证该隐私保护机制具有较低的计算开销。     

SaaS环境下的数据隐私保护机制研究

在软件即服务(SaaS)环境下,由于租户数据统一保存在云中,因而具有隐私泄露的风险。综合考虑数据使用效率和数据隐私保护两个方面,提出一种租户数据隐私保护策略。首先,根据日志数据进行属性聚类,使得关联度较高的属性聚集到一起,以确保数据应用效率;然后,根据隐私约束条件对聚类后的每个属性集合做进一步的划分,生成可以避免数据隐私泄露的数据分割策略;最后,利用第三方的可信云实现数据隐私分割策略的保存,并依此进行租户数据表的分割和重构。对比实验结果表明,提出的数据隐私分割算法整体来说具有较好的性能,可以实现100%的隐私保护效果。     

面向关联属性的差分隐私信息熵度量方法

针对差分隐私非交互式多属性关联的合成数据集发布问题,基于信息熵、汉明失真提出了发布数据集隐私度、数据效用、隐私泄露风险的量化方法.首先,利用互信息量分析属性相关度,并以关联依赖图模型表达属性关联.其次,基于图中关键隐私泄露路径构建马尔可夫隐私泄露链,并结合信息熵提出一种关联属性隐私度量模型及方法,可以有效的度量由关联属性引起的隐私泄露量.最后,通过具体实例验证了模型与方法的有效性,并对比分析了该方法的优势.     

一种增强的个性化匿名隐私保护模型

匿名模型是近年来隐私保护研究的热点技术之一,主要研究如何在数据发布中避免敏感数据的泄露,又能保证数据发布的高效用性.提出了一种个性化（α[s],l）-多样k-匿名模型,该方法将敏感属性泛化成泛化树,根据数据发布中隐私保护的具体要求,给各结点设置不同的α约束,发布符合个性化匿名模型的数据.该方法在保护隐私的同时进一步提高信息的个性化要求.实验结果表明,该方法提高了信息的有效性,具有很高的实用性.     

局部差分隐私约束的关联属性不变后随机响应扰动

本文研究敏感属性与部分准标识符属性存在相关时,如何有效减小重构攻击导致的隐私泄漏风险.首先,用互信息理论寻找原始数据集中对敏感属性具有强依赖关系的准标识符属性,为精确扰动数据属性提供理论依据;其次,针对关联属性和非关联属性,应用不变后随机响应方法分别对某个数据属性或者属性之间的组合进行扰动,使之满足局部ε-差分隐私要求,并理论分析后数据扰动对隐私泄露概率和数据效用的影响;最后,实验验证所提算法的有效性和处理增量数据的能力,理论分析了数据结果.由实验结果可知,算法可以更好地达到数据效用和隐私保护的平衡.     

无可信第三方的数据匿名化收集协议

针对半诚信的数据收集者对包含敏感属性(SA)数据收集和使用过程中可能造成隐私泄露问题,该文在传统模型中增加实时的数据领导者,并基于改进模型提出一个隐私保护的数据收集协议,确保无可信第三方假设前提下,数据收集者最大化数据效用只能建立在K匿名处理过的数据基础上。数据拥有者分布协作的方式参与协议流程,实现了准标识(QI)匿名化后SA的传输,降低了数据收集者通过QI关联准确SA值的概率,减弱内部标识揭露造成隐私泄露风险;通过树形编码结构将SA的编码值分为随机锚点和补偿距离两份份额,由K匿名形成的等价类成员选举获取两个数据领导者,分别对两份份额进行聚集和转发,解除唯一性的网络标识和SA值的关联,有效防止外部标识揭露造成的隐私泄露;建立符合该协议特性的形式化规则并对协议进行安全分析,证明了协议满足隐私保护需求。     

基于k-模糊匿名的隐私保护

在发布数据的隐私保护过程中,准标识符和敏感属性信息是隐私保护的主要内容。但无论是K-匿名还是基于模糊集的隐私保护方法,都不能有效地对准标识符和敏感属性信息进行保护。针对上述问题,提出了新型的K-模糊匿名隐私保护算法。并通过实验进行了验证,相比于其它两种方法,基于k-模糊匿名的隐私保护方法能更有效保护准标识符和敏感属性信息,信息损失更少,发布数据的可用性更好。     

聚类的(α,κ)-匿名数据发布

为更好的抵御背景知识攻击和同质攻击,保护特定的敏感值或全部敏感值,定义了单敏感值(α,κ)-匿名模型和多敏感值(α,κ)-匿名模型,并分别设计了两个聚类算法予以实现,同时分析了算法的正确性和复杂性.对于即包含连续属性又包含分类属性的数据集,给出了数据集的详细映射与处理方法,使数据集中点的距离可以方便的计算,彻底避免了把...     

An Improved Algorithm of Individuation K-Anonymity for Multiple Sensitive Attributes

At present, most of privacy preserving approaches in data publishing are applied to single sensitive attribute. However, applying single-sensitive-attribute privacy preserving techniques directly into data with multiple sensitive attributes often causes leakage of large amount of private information. This paper focuses on the privacy preserving methods in data publishing for multiple sensitive attributes. It combines data anonymous methods based on lossy join with the idea of clustering. And it proposes an improved algorithm of individuation K-anonymity for multiple sensitive attributes—\( MSA(\alpha ,l) \) algorithm. By setting parameters \( \alpha \) and \( l \), it can restrain sensitive attribute values in equivalence class, to make a more balanced distribution of sensitive attributes and satisfy the demand of diversity, then this algorithm is applied to K-anonymity model. Finally, the result of experiment shows that this improved model can preserve the privacy of sensitive data, and it can also reduce the information hidden rate.     

Delay‐aware privacy‐preserving location‐based services under spatiotemporal constraints

The ubiquitous use of location‐based services (LBS) through smart devices produces massive amounts of location data. An attacker, with an access to such data, can reveal sensitive information about users. In this paper, we study location inference attacks based on the probability distribution of historical location data, travel time information between locations using knowledge of a map, and short and long‐term observation of privacy‐preserving queries. We show that existing privacy‐preserving approaches are vulnerable to such attacks. In this context, we propose a novel location privacy‐preserving approach, called KLAP, based on the three fundamental obfuscation requirements: minimum k ‐locations, l ‐diversity, and privacy a rea p reservation. KLAP adopts a personalized privacy preference for sporadic, frequent, and continuous LBS use cases. Specifically, it generates a secure concealing region (CR) to obfuscate the user's location and directs that CR to the service provider. The main contribution of this work is twofold. First, a CR pruning technique is devised to establish a balance between privacy and delay in LBS usage. Second, a new attack model called a long‐term obfuscated location tracking attack, and its countermeasure is proposed and evaluated both theoretically and empirically. We assess KLAP with two real‐world datasets. Experimental results show that it can achieve better privacy, reduced delay, and lower communication costs than existing state‐of‐the‐art methods.     

一种改进的基于奇异值分解的隐私保持分类挖掘方法

 隐私保护是数据挖掘研究的重要内容之一,目前已经提出了大量隐私保持的数据挖掘算法.基于奇异值分解的方法是其中重要的一种,它是一种基于数据扰动的方法.现有的基于奇异值分解的隐私保持数据挖掘方法对所有样本和属性都进行同样强度的扰动.但不同的样本和属性可能对隐私保护有不同的要求,而且对数据挖掘的重要性也可能不同,因此最好可以对他们进行不同程度的扰动.本文对基于奇异值分解的数据扰动方法进行改进,使之可以对不同的样本和属性进行不同程度的扰动.并在此基础上提出了一种改进的隐私保持分类挖掘方法.实验表明,与原有的基于奇异值分解的方法相比,在保证数据可用性的前提下,本文方法可以对隐私数据提供更好的保护.     

基于交互模式的隐私保护方法研究

In network environments, before meaningful interactions can begin, trust may need to be established between two interactive entities in which an entity may ask the other to provide some information involving privacy. Consequently, privacy protection and trust establishment become important in network interactions. In order to protect privacy while facilitating effective interactions, we propose a trust based privacy protection method. Our main contributions in this paper are as follows: (1) We introduce a novel concept of k sensitive privacy as a measure to assess the potential threat of inferring privacy; (2) According to trust and k sensitive privacy evaluation, our proposed method can choose appropriate interaction patterns with lower degree of inferring privacy threat; (3) By considering interaction patterns for privacy protection, our proposed method can overcome the shortcomings of some current privacy protection methods which may result in low interaction success rate. Simulation results show that our method can achieve effective interactions with less privacy loss.     

Personalized privacy protection method for group recommendation

To address the problem that most of the existing privacy protection methods can not satisfy the user’s personalized requirements very well in group recommendation,a user personalized privacy protection framework based on trusted client for group recommendation (UPPPF-TC-GR) followed with a group sensitive preference protection method (GSPPM) was proposed.In GSPPM,user’s historical data and privacy preference demands were collected in the trusted client,and similar users were selected in the group based on sensitive topic similarity between users.Privacy protection for users who had privacy preferences in the group was realized by randomization of cooperative disturbance to top k similar users.Simulation experiments show that the proposed GSPPM can not only satisfy privacy protection requirements for each user but also achieve better performance.     

Dynamic searchable encryption with privacy protection for cloud computing

The dynamic searchable encryption schemes generate search tokens for the encrypted data on a cloud server periodically or on a demand. With such search tokens, a user can query the encrypted data whiles preserving the data's privacy; ie, the cloud server can retrieve the query results to the user but do not know the content of the encrypted data. A framework DSSE with Forward Privacy (dynamic symmetric searchable encryption [DSSE] with forward privacy), which consists of Internet of Things and Cloud storage, with the attributes of the searchable encryption and the privacy preserving are proposed. Compared with the known DSSE schemes, our approach supports the multiusers query. Furthermore, our approach successfully patched most of the security flaws related to the sensitive information's leakage in the DSSE schemes. Both security analysis and simulations show that our approach outperforms other DSSE schemes with respect to both effectiveness and efficiency.