Interval-valued intuitionistic fuzzy mathematical programming method for hybrid multi-criteria group decision making with interval-valued intuitionistic fuzzy truth degrees

As an important component of group decision making, the hybrid multi-criteria group decision making (MCGDM) is very complex and interesting in real applications. The purpose of this paper is to develop a novel interval-valued intuitionistic fuzzy (IVIF) mathematical programming method for hybrid MCGDM considering alternative comparisons with hesitancy degrees. The subjective preference relations between alternatives given by each decision maker (DM) are formulated as an IVIF set (IVIFS). The IVIFSs, intuitionistic fuzzy sets (IFSs), trapezoidal fuzzy numbers (TrFNs), linguistic variables, intervals and real numbers are used to represent the multiple types of criteria values. The information of criteria weights is incomplete. The IVIFS-type consistency and inconsistency indices are defined through considering the fuzzy positive and negative ideal solutions simultaneously. To determine the criteria weights, we construct a novel bi-objective IVIF mathematical programming of minimizing the inconsistency index and meanwhile maximizing the consistency index, which is solved by the technically developed linear goal programming approach. The individual ranking order of alternatives furnished by each DM is subsequently obtained according to the comprehensive relative closeness degrees of alternatives to the fuzzy positive ideal solution. The collective ranking order of alternatives is derived through establishing a new multi-objective assignment model. A real example of critical infrastructure evaluation is provided to demonstrate the applicability and effectiveness of this method.     

Multi-criteria group decision making based on trapezoidal intuitionistic fuzzy information

With respect to multi-criteria group decision making (MCGDM) problems under trapezoidal intuitionistic fuzzy environment, a new MCGDM method is investigated. The proposed method can effectively avoid the failure caused by the use of inconsistent decision information and provides a decision-making idea for the case of “the truth be held in minority”. It consists of three interrelated modules: weight determining mechanism, group consistency analysis, and ranking and selection procedure. For the first module, distance measures, expected values and arithmetic averaging operator for trapezoidal intuitionistic fuzzy numbers are used to determine the weight values of criteria and decision makers. For the second module, a consistency analysis and correction procedure based on trapezoidal intuitionistic fuzzy weighted averaging operator and OWA operator is developed to reduce the influence of conflicting opinions prior to the ranking process. For the third module, a trapezoidal intuitionistic fuzzy TOPSIS is used for ranking and selection. Then a procedure for the proposed MCGDM method is developed. Finally, a numerical example further illustrates the practicality and efficiency of the proposed method.     

A group decision making framework based on fuzzy VIKOR approach for machine tool selection with linguistic information

Computer numerical control (CNC) machines are used for repetitive, difficult and unsafe manufacturing tasks that require a high degree of accuracy. However, when selecting an appropriate CNC machine, multiple criteria need to be considered by multiple decision makers. In this study, a multi-criteria group decision making (MCGDM) technique based on the fuzzy VIKOR method is developed to solve a CNC machine tool selection problem. Linguistic variables represented by triangular fuzzy numbers are used to reflect decision maker preferences for the criteria importance weights and the performance ratings. After the individual preferences are aggregated or after the separation values are computed, they are then defuzzified. In this paper, two algorithms based on a fuzzy linguistic approach are developed. Based on these two algorithms and the VIKOR method, a general MCGDM framework is proposed. A CNC machine tool selection example illustrates the application of the proposed approach. A comparative study of the two algorithms using the above case study information highlighted the need to combine the ranking results, as both algorithms have distinct characteristics.     

An integrated probabilistic linguistic projection method for MCGDM based on ELECTRE III and the weighted convex median voting rule

In the multi-criteria group decision-making (MCGDM) problems with great uncertainty, making full use of participants' evaluation information could help improve the accuracy and reliability of decision results. Probabilistic linguistic term set (PLTS) is an effective tool to represent qualitative data and can fully express the hesitation and preference of decision makers. Therefore, this paper aims to propose an MCGDM method based on PLTSs. In the proposed method, the projection of PLTSs is explored to measure the distance and angle differences between two objects, and Bayesian best–worst method (Bayesian BWM) is used to determine the aggregated final weights of criteria. Besides, the elimination and choice translating reality III (ELECTRE III) method combined with distillation algorithm deals with the projection of PLTSs to obtain the alternatives' ranking of each decision maker. Then, the weighted convex median voting rule is developed to integrate the rankings results regarding all decision makers, which can solve the conflict of ranking results among experts and ensure that the comprehensive ranking results are reasonable and practical. Finally, a case study of health-care waste management is designed and comparative analyses are implemented to show the effectiveness and advantages of the proposed method.     

基于查询概率的位置隐私保护方法

现有的隐私保护技术较少考虑到查询概率、map数据、信息点（POI）语义等边信息,攻击者可以将边信息与位置数据相结合推断出用户的隐私信息,为此提出一种新的方法ARB来保护用户的位置隐私。该方法首先把空间划分为网格,根据历史查询数据计算出处于不同网格区域的用户提交查询的概率;然后结合相应单元格的查询概率来生成用户匿名区域,从而保护用户的位置隐私信息;最后采用位置信息熵作为隐私保护性能的度量指标。在真实数据集上与已有的两种方法进行对比来验证隐私保护方法的性能,结果显示该方法具体有较好的隐私保护效果和较低的时间复杂度。     

Multi‐criteria group decision‐making method based on TODIM with probabilistic interval‐valued hesitant fuzzy information

Probabilistic interval‐valued hesitant fuzzy sets (PIV‐HFSs) are suitable for aggregating information from different groups because the probabilistic information of all the groups can be included by using interval values. Moreover, decision makers (DMs) prefer to use interval values to provide evaluation information. Furthermore, the traditional multi‐criteria group decision‐making (MCGDM) approach has some limitations, such as obtaining the DMs' weights with inappropriate methods and neglecting the interactions amongst the criteria and the psychological characteristics of DMs. Motivated by these research background, the main contents of this study are as follows. First, PIV‐HFSs are proposed, and the convex combination operation is extended into PIV‐HFSs. Second, a hybrid MCGDM approach with PIV‐HFSs is suggested that is based on the maximizing deviation method, fuzzy analytic network process (FANP) and TODIM (an acronym in Portuguese for interactive and multi‐criteria decision‐making model). Third, an evaluation case of health management centres based on the service‐specific failure mode and effect analysis (FMEA) is considered. The results show that the most crucial secondary factor is frequency (0.35775) and that the most serious failure mode is the inaccurate check‐in. The results demonstrate that the proposed model can evaluate service quality effectively and that it performs better than other methods.     

基于CP-ABE算法的区块链数据访问控制方案

与公有链不同,联盟区块链超级账本Fabric额外集成了成员管理服务机制,能够提供基于通道层面的数据隔离保护。但这种数据隔离保护机制在通道内同步的仍是明文数据,因此存在一定程度的数据泄露风险。另外,基于通道的数据访问控制在一些细粒度隐私保护场景下也不适用。为了解决上述提及的联盟链超级账本中存在的数据隐私安全问题,提出了一种基于CP-ABE算法的区块链数据访问控制方案。结合超级账本中原有的Fabric-CA模块,提出的方案在实现用户级细粒度安全访问控制区块链数据的同时,还能够实现对CP-ABE方案中用户属性密钥的安全分发。对该方案进行的安全分析表明,该方案实现了ABE用户属性私钥安全分发和数据隐私性保护的安全性目标,性能分析部分也说明了所提方案具有良好的可用性。     

社交网络用户隐私泄露量化评估方法

社交网络用户隐私泄露的量化评估有利于帮助用户了解个人隐私泄露状况,提高公众隐私保护和防范意识,同时也能为个性化隐私保护方法的设计提供依据.针对目前隐私量化评估方法主要用于评估隐私保护方法的保护效果,无法有效评估社交网络用户的隐私泄露风险的问题,提出了一种社交网络用户隐私泄露量化评估方法.基于用户隐私偏好矩阵,利用皮尔逊相似度计算用户主观属性敏感性,然后取均值得到客观属性敏感性;采用属性识别方法推测用户隐私属性,并利用信息熵计算属性公开性;通过转移概率和用户重要性估计用户数据的可见范围,计算数据可见性;综合属性敏感性、属性公开性和数据可见性计算隐私评分,对隐私泄露风险进行细粒度的个性化评估,同时考虑时间因素,支持用户隐私泄露状况的动态评估,为社交网络用户了解隐私泄露状况、针对性地进行个性化隐私保护提供支持.在新浪微博数据上的实验结果表明,所提方法能够有效地对用户的隐私泄露状况进行量化评估.     

基于连续查询的用户轨迹k-匿名隐私保护算法

随着移动服务和移动网络的持续发展,基于LBS的连续查询服务被广泛应用。基于单点的K-匿名位置隐私保护算法已经不能满足连续查询下用户位置隐私需求。针对用户轨迹隐私保护提出新的保护方法,该方法采用不可信第三方中心匿名器,用户获取自己的真实位置后首先在客户端进行模糊处理,然后提交给第三方匿名器,第三方匿名器根据用户的隐私需求结合用户某时刻的真实位置信息生成虚假用户,然后根据历史数据生成虚假轨迹。为了进一步提高虚假轨迹与用户真实轨迹的相似性,该算法提出了虚假轨迹生成的两个约束条件：虚假轨迹距用户真实轨迹的距离约束和相似性约束。经大量实验证明,该算法与传统的不同时刻K-匿名算法相比,不仅可以满足连续查询的用户轨迹隐私保护而且可以满足基于快照的LBS用户位置隐私保护。     

差分隐私保护下的数据分级发布机制

隐私保护技术解决了数据发布过程中的隐私泄露问题,然而当前的数据发布技术大多只面向查询用户发布同一隐私保护级别的数据,并未考虑查询用户等级不同的情况。在所提出的满足差分隐私的数据分级发布机制中,数据发布方利用隐私预算参数不同的拉普拉斯机制对数据查询结果进行隐私保护处理,实现了输出隐私保护程度不同的查询结果。在依据付费或权限对查询用户分级后,数据发布方为等级较高（低）的查询用户发布隐私保护程度较低（高）的查询结果,使得查询用户可使用错误率较低（高）的数据,达到了隐私数据分级发布的效果。实验结果与安全性分析表明该机制在抵抗背景知识攻击的同时还可有效地实现输出错误率不同的分级查询结果。     

社交网络用户浏览轨迹信息隐私保护方法仿真

随着网络应用的广泛普及,QQ、微信、YY语音、陌陌等社交软件走进千家万户,但社交网络用户浏览轨迹信息隐私保护问题也随之而来。由于社交网络平台安全机制存在漏洞,抵御网络攻击性能不强,使社交网络用户信息纷纷泄露。针对问题根源,提出ACP用户隐私信息防护系统,建立社交网络用户真空登陆模块(VM)、通讯信息密码文模块(RDT)及信息储存保护墙模块(LDM)一体化ACP用户隐私信息防护系统,从根源保护社交网络用户浏览轨迹信息的隐私安全。通过数据模拟仿真实验证明提出的ACP用户隐私信息防护系统,对社交网络用户浏览轨迹信息隐私保护具有可用性与有效性。     

服务组合安全隐私信息流静态分析方法

用户为使用服务组合提供的功能,需要提供必要的个人隐私数据.由于组合的业务逻辑对用户是透明的,且用户与成员服务之间缺乏隐私数据使用的相关协议,如何保证组合执行过程中不发生用户隐私信息的非法泄露,成为当前服务计算领域的研究热点之一.针对隐私保护特征,提出一种服务组合安全隐私信息流静态分析方法.首先,从服务信誉度、隐私数据使用目的及保留期限这3个维度提出一种面向服务组合的隐私信息流安全模型;其次,采用支持隐私信息流分析的隐私工作流网（privacy workflow net,简称PWF-net）构建服务组合模型,并通过静态分析算法分析组合执行路径,检测组合的执行是否会发生用户隐私信息的非法泄露;最后,通过实例分析说明了方法的有效性,并对方法性能进行了实验分析.与现有的相关工作相比,针对隐私保护特征提出了隐私信息流安全模型,且分析方法考虑了隐私数据项聚合问题,从而能够更为有效地防止用户隐私信息非法泄露.     

Determining trust in media-rich websites using semantic similarity

Significant growth of multimedia content on the World Wide Web (or simply ??Web??) has made it an essential part of peoples lives. The web provides enormous amount of information, however, it is very important for the users to be able to gauge the trustworthiness of web information. Users normally access content from the first few links provided to them by search engines such as Google or Yahoo!. This is assuming that these search engines provide factual information, which may be popular due to criteria such as page rank but may not always be trustworthy from the factual aspects. This paper presents a mechanism to determine trust of websites based on the semantic similarity of their multimedia content with already established and trusted websites. The proposed method allows for dynamic computation of the trust level of websites of different domains and hence overcomes the dependency on traditional user feedback methods for determining trust. In fact, our method attempts to emulate the evolving process of trust that takes place in a user??s mind. The experimental results have been provided to demonstrate the utility and practicality of the proposed method.     

面向移动社交网络的位置隐私保护方法

移动社交网络为人们的生活带来了极大的便利,但用户在享受这些服务带来便利的同时,个人位置隐私受到了严重威胁。首先对用户位置隐私保护需求进行了形式化描述,继而针对用户的敏感兴趣点泄露问题,提出了一种情景感知的隐私保护方法。该方法将位置信息、社交关系、个人信息引入到知识构建算法中以计算兴趣点间的相关性,并利用该相关性及时空情景实时判断发布当前位置是否会泄露用户隐私,进而实现了隐私保护与服务可用性间的平衡。最后通过仿真实验验证了该方法的有效性。     

k-匿名下通过本地差分隐私实现位置隐私保护

针对用户位置隐私保护过程中攻击者利用背景知识等信息发起攻击的问题,提出一种面向移动终端的位置隐私保护方法。该方案通过利用k-匿名和本地差分隐私技术进行用户位置保护,保证隐私和效用的权衡。结合背景知识构造匿名集,通过改进的Hilbert曲线对k-匿名集进行分割,使用本地差分隐私算法RAPPOR扰动划分后的位置集,最后将生成的位置集发送给位置服务提供商获取服务。在真实数据集上与已有的方案从用户位置保护、位置可用性和时间开销方面进行对比,实验结果显示,所提方案在确保LBS服务质量的同时,也增强了位置隐私保护的程度。     

Product ranking using hierarchical aspect structures

Product-related comparative reviews are one of the most helpful information sources for consumers to rank the competing products and make purchase decisions. The comparative reviews are even more valuable if they are concerning specific aspects. However, the fact that very few comparative reviews express direct opinions on product aspects makes aspect-based product ranking a difficult task. In this paper, we present a novel hierarchical aspect-based product ranking approach. We first mine aspect-based pairwise comparative opinions from both user reviews on multiple review websites and community-based question answering pairs containing product comparison information. Next, we use our hierarchical structure-based model to propagate and reassign the aspect-based comparative opinions by using the parent-child and sibling relations between aspects in the product aspect hierarchy. The structure-based model helps to address the data sparsity issue of very few or no comparative reviews for some aspects. Finally, we employ graph-based ordering algorithms to consolidate these reassigned pairwise opinions into listwise comparison results. Experiments on a set of candidate electronic products prove that the proposed approach is effective for aspect-based product ranking.     

A PLA-based privacy-enhancing user modeling framework and its evaluation

Reconciling personalization with privacy has been a continuing interest in user modeling research. This aim has computational, legal and behavioral/attitudinal ramifications. We present a dynamic privacy-enhancing user modeling framework that supports compliance with users’ individual privacy preferences and with the privacy laws and regulations that apply to each user. The framework is based on a software product line architecture. It dynamically selects personalization methods during runtime that meet the current privacy constraints. Since dynamic architectural reconfiguration is typically resource-intensive, we conducted a performance evaluation with four implementations of our system that vary two factors. The results demonstrate that at least one implementation of our approach is technically feasible with comparatively modest additional resources, even for websites with the highest traffic today. To gauge user reactions to privacy controls that our framework enables, we also conducted a controlled experiment that allowed one group of users to specify privacy preferences and view the resulting effects on employed personalization methods. We found that users in this treatment group utilized this feature, deemed it useful, and had fewer privacy concerns as measured by higher disclosure of their personal data.     

基于加密分割的位置隐私保护方法

基于位置的服务(location-based services,LBS)由于存在隐私泄露问题已越来越成为隐私保护领域中的热点.针对用户协作的隐私保护方法无法为参与用户提供自定义匿名度功能,且对协作用户的隐私保护效力不足,提出了一种基于加密分割的位置隐私保护方法.该方法采用分布式结构,通过用户对查询信息进行分割、加密、交换混合,满足了用户自定义匿名度需求,提高了用户间的隐私安全性,同时,采用假名方法抵御长期统计的攻击方式.通过理论分析和实验结果表明所提出的方法具有较好的隐私保护效果.     

A trustworthy online recommendation system based on social connections in a privacy-preserving manner

Certain consumer websites provide reviews from previous buyers to help new customers make purchasing decisions. However, fake reviews can have an adverse impact on user trust. Most previous suggestions for addressing this problem are still subject to various security concerns in terms of privacy, reliability, and authenticity. To ensure the security of online review systems, this paper proposes the development of a secure online-evaluation method based on social connections to establish evaluation authenticity and provide protection against evaluation forgery while preserving the reviewer’s identity. The proposed method enables users to recognize evaluations from their friends to identify reviews from more trustworthy sources, and authenticates online reviews to prevent possible forgery. In addition, it preserves the privacy of friendship relationships from application server and other users and identifier relations between the personal identifier and online identifier. The proposed approach can be applied to Internet auctions and online games, and is shown to be secure and efficient, with sufficient matching probability to be practical.     

一种基于匿名区域变换的位置隐私保护方法

针对基于位置服务的应用中存在的用户位置隐私泄露问题,提出一种基于匿名区域变换的位置隐私保护方法。在离用户一定距离处选择一个锚点生成匿名区域后,利用邻近节点处理法计算用户邻近节点查询结果与用户真实位置之间的距离,从而实现在保护用户位置隐私的同时得到精确的查询结果。理论分析和实验结果表明,与Cloaking Region和SpaceTwist算法相比,该方法在保证较低通信开销的前提下,具有较好的位置隐私保护性能。