数据发布中的个性化隐私匿名技术研究

个性化隐私保护是目前数据发布中隐私泄露控制技术研究的热点问题之一。对这方面的研究现状进行综述。首先,在分析不同类型个性化服务需求的基础上,建立相应的个性化隐私匿名模型;其次,根据采用技术的不同,对已有的个性化隐私保护匿名技术进行总结,并对各类技术的基本原理、特性进行概括性的阐述。同时,根据算法所采用信息度量的差异,给出现有个性化隐私度量的方法与标准。最后,在对比分析已有研究的基础上,总结全文并展望了个性化隐私保护匿名技术的进一步研究方向。     

面向移动终端情景隐私保护设计研究

基于情境的个性化服务已经广泛应用在人们的日常生活中,并且在不断扩展服务的应用范围。然而,在这些 个性化服务体验的背后,也隐藏着不容忽视的隐私问题。因此,研究情景隐私的保护有迫切的现实需要。本论文重新分析移 动端重情境隐私保护的隐私模型,突破当前情境隐私保护的狭隘概念,完善情境隐私保护的理论基础,提出新的情境隐私的保 护方法,拟将解决当前情境隐私保护中理论基础不完善的现状,促进密码学、网络安全与软件安全技术的发展与情境隐私保护 方法的实用化。     

一种基于最小选择度优先的多敏感属性个性化l-多样性算法

数据发布中的隐私保护技术一直是数据挖掘与信息安全领域关注的重要问题.目前大部分的研究都仅限于单敏感属性的隐私保护技术,而现实生活中存在着大量包含多敏感属性的数据信息.同时,随着个性需求的不断提出,隐私保护中的个性化服务越来越受研究者的关注.为了扩展单敏感属性数据的隐私保护技术以及满足个性化服务的需求问题,研究了数据发布过程中面向多敏感属性的个性化隐私保护方法.在单敏感属性l-多样性原则的基础上,引入基于值域等级划分的个性化定制方案,定义了多敏感属性个性化l-多样性模型,并提出了一种基于最小选择度优先的多敏感属性个性化l-多样性算法.实验结果表明:该方法不仅可以满足隐私个性化的需求,而且能有效地保护数据的隐私,减少信息的隐匿率,保证发布数据的可用性.     

个性化时空数据隐私保护

智能移动终端的普及导致收集的时空数据中个人位置隐私、签到数据隐私、轨迹隐私等敏感信息容易泄露,且当前研究分别针对上述隐私泄露单独提出保护技术,而没有面向用户给出防止上述隐私泄露的个性化时空数据隐私保护方法。针对这个问题,提出一种面向时空数据的个性化隐私保护模型（p,q,ε）-匿名和基于该模型的个性化时空数据隐私保护（PPP_ST）算法,从而对用户个性化设置的隐私数据（位置隐私、签到数据隐私和轨迹隐私）加以保护。设计了启发式规则对时空数据进行泛化处理,保证了发布数据的可用性并实现了时空数据的高可用性。对比实验中PPP_ST算法的数据可用率比个性化信息数据K-匿名（IDU-K）和个性化Clique Cloak（PCC）算法分别平均高约4.66%和15.45%。同时,设计了泛化位置搜索技术来提高算法的执行效率。基于真实时空数据进行实验测试和分析,实验结果表明PPP_ST算法能有效地保护个性化时空数据隐私。     

一个普适计算环境中的定位隐私保护模型*

随着普适计算技术的发展,在普适环境中的应用服务也日益增多。这些服务通常都是基于用户位置的服务,然而,用户的定位信息中往往直接或间接地包含着用户的隐私内容。针对如何在获取个性化定制服务的同时保护用户的定位隐私这一问题展开讨论,提出了一种定位信息采集和假名使用的策略,并基于该策略建立了一个普适计算环境中的定位隐私保护模型。     

个性化推荐系统隐私保护策略研究进展

个性化推荐系统能较好地帮助用户获得个人所需的信息,但它要获得好的推荐效果,需要收集大量的用户个人信息;这些信息可能泄露个人隐私,用户会因对隐私泄露的担心而放弃对推荐系统的信任,所以大量的研究集中于如何在获得高效推荐的同时保护用户的个人隐私。主要就个性化推荐系统中使用的隐私保护技术进行了综述,在给出了隐私和隐私保护定义的同时讨论了隐私保护的相关技术,包括隐私策略描述语言和目前使用的隐私保护技术。最后尝试给出了今后的研究重点和方向。     

车联网中基于位置服务的个性化位置隐私保护

随着车联网的快速发展,用户享受车联网提供的位置服务(location-based services,LBSs)时,位置隐私泄漏是一个关键安全问题.针对车载网络中位置服务隐私泄露问题,提出了一种基于差分隐私的个性化位置隐私保护方案,在保护用户隐私的前提下,满足用户个性化隐私需求.首先,定义归一化的决策矩阵,描述导航推荐路线的效率和隐私效果;然后,引入多属性理论,建立效用模型,将用户的隐私偏好整合到该模型中,为用户选择效益最佳的驾驶路线;最后,考虑到用户的隐私偏好需求,以距离占比为衡量指标,为用户分配合适的隐私预算,并确定虚假位置的生成范围,以生成效用最高的服务请求位置.基于真实数据集,通过仿真实验,将所提方案与现有方案进行对比,实验结果表明:所提出的个性化位置隐私保护方案在合理保护用户隐私的情况下,能够满足用户的服务需求,以提供更高的服务质量(quality of service, QoS).     

个性化(α,l)-多样性k-匿名隐私保护模型

针对传统隐私保护模型对个性化匿名缺乏考虑的问题,对现有的两种个性化匿名机制进行了分析。在k-匿名和l-多样性匿名模型的基础上,提出一种个性化(α,l)-多样性k-匿名模型来解决存在的问题。在该模型中,依据敏感程度的不同,对敏感属性的取值划分类别;设置相应的约束条件,并为特定的个体提供个性化的隐私保护。实验结果表明,所提模型在有效提供个性化服务的同时,具有更强的隐私保护能力。     

基于云联邦的差分隐私保护动态推荐模型

为提高推荐系统的准确性和个性化水平,同时保护用户的隐私,文章提出一种基于云联邦的差分隐私保护动态推荐模型（P2RCF）。该模型采用注意力机制动态调整融合长短期用户兴趣,增强推荐系统的灵活性,同时引入差分隐私技术和云联邦技术保护用户的隐私信息。文章在公共数据集上进行了实验,实验结果表明,该模型可以在保护用户数据隐私的同时提高推荐的准确性和个性化水平。     

一种个性化移动社交网络轨迹隐私保护方案*

针对移动社交网络迅猛发展带来的发布轨迹隐私泄露问题,提出了一种个性化的轨迹保护方案。根据个体个性化的隐私保护需求差异,对不同个体采用了不同的保护准则,这样可以解决传统隐私保护下“过度保护”及轨迹效用低等问题。给出k敏感轨迹匿名和(k,p)敏感轨迹匿名等重要的隐私保护定义,并利用Trie树的构造、剪枝、重构等技术实现了个体的个性化隐私保护。最后,通过在真实数据集上的实验分析,证明该个性化方案比现存隐私保护方案在轨迹位置损失率方面性能优,计算延时较低和效率高。     

Willing to pay for quality personalization? Trade-off between quality and privacy

Online personalization presents recommendations of products and services based on customers’ past online purchases or browsing behavior. Personalization applications reduce information overload and provide value-added services. However, their adoption is hindered by customers’ concerns about information privacy. This paper reports on research undertaken to determine whether a high-quality recommendation service will encourage customers to use online personalization. We collected data through a series of online experiments to examine the impacts of privacy and quality on personalization usage and on users’ willingness to pay and to disclose information when using news and financial services. Our findings suggest that under certain circumstances, perceived personalization quality can outweigh the impact of privacy concerns. This implies that service providers can improve the perceived quality of personalization services being offered in order to offset customer privacy concerns. Nevertheless, the impact of perceived quality on personalization usage is weaker for customers who have experienced privacy invasion in the past. The results show that customers who are likely to use online personalization are also likely to pay for the service. This finding suggests that, despite privacy concerns, there is an opportunity for businesses to monetize high-quality personalization.     

粒化（a，k）-匿名方法研究

针对现有个性化隐私匿名技术不能同时满足面向个体需求的个性化和面向敏感属性值的个性化两方面的要求,引入了粒计算思想。建立隐私保护决策度集合,以刻画不同个体对敏感属性同一敏感值的不同保护要求;基于决策度集合的不同取值建立顶层粒度空间;对每个顶层粒度空间中敏感值赋予不同的出现频率约束,以满足面向敏感值的个性化匿名需求。算法分析及仿真实验结果表明,粒化（a,k）-匿名模型和算法以较小的信息损失和执行时间获得更综合、更合理的个性化隐私保护的实现。     

车联网隐私保护技术研究

随着汽车智能化、网联化程度的不断加深,车辆、用户及第三方机构之间的数据共享日益成为刚需,由车辆、用户、路边单元等通信实体之间构建的网络车联网应运而生,而车联网的高移动性和网络拓扑多变性使其更容易遭受攻击,进而导致严重的车联网用户隐私泄露问题。如何平衡数据共享和隐私保护之间的关系成为车联网产业发展所面临的一个关键挑战。近年来,学术界针对车联网隐私保护问题进行了深入的研究,并提出了一系列解决方案,然而,目前缺少对这些方案从隐私属性方面进行分析。为此,本文首先从车联网的系统架构、通信场景及标准进行阐述。然后对车联网隐私保护的需求、攻击模型及隐私度量方法进行分析与总结。在此基础上从车联网身份隐私、匿名认证位置隐私和车联网位置服务隐私三个方面出发,介绍了匿名认证、假名变更、同态加密、不经意传输等技术对保护车联网用户隐私起到的重要作用,并讨论了方案的基本原理及代表性实现方法,将方案的隐私性从不可链接性、假名性、匿名性、不可检测性、不可观察性几个方面进行了分析与总结。最后探讨了车联网隐私保护技术当前面临的挑战及进一步研究方向,并提出了去中心化的车辆身份隐私技术以保护车辆身份隐私、自适应假名变更技术以支持匿名认证、满足个性化隐私需求的位置服务隐私保护技术,以期望进一步推动车联网隐私保护技术研究的发展与应用。     

基于LBS系统的位置轨迹隐私保护技术综述

基于位置的服务（LBS）方式为用户生活带来了便捷,同时也存在着位置轨迹隐私泄露的风险。如何对这些隐私数据加以保护,成为目前研究者的研究热点。对现有的隐私保护方法系统架构进行总结,对位置隐私保护技术和轨迹隐私保护技术进行综述,分类介绍了基于扭曲技术、加密技术、匿名技术以及差分隐私技术的隐私保护方法,对各种隐私保护技术下的最新算法进行整理归纳。根据对已有技术的了解,提出了现有研究中存在的不足以及未来研究的方向。     

Differential responses of loyal versus habitual consumers towards mobile site personalization on privacy management

We examine how two different underlying mechanisms of behavioral loyalty to a brand—attitudinal loyalty and habit—impact smartphone users' privacy management when they browse personalized vs. non-personalized mobile websites. The online experimental study conducted with Amazon Mechanical Turk workers (N = 73) finds different responses of attitudinal loyalty and habit towards personalization in significant three-way interactions between personalization, attitudinal loyalty, and habit on privacy disclosure and protection behaviors. When interacting with a personalized website, highly habitual consumers without high level of attitudinal loyalty disclosed the most personal information on a personalized mobile site, and displayed the least intention of protecting their privacy on their smartphones, whereas consumers with high levels of both habit and attitudinal loyalty reported the highest tendency of privacy protection behavior. However, habit and personalization do not have a significant effect on disclosure behaviors when users have high attitudinal loyalty to a brand. Theoretical and practical implications are discussed.     

E-service quality competition through personalization under consumer privacy concerns

One important factor that determines the quality of web-based customer service is the ability of a firm’s website to provide individual caring and attention. In this sense, online vendors try to offer varieties of web-based personalization. However, many previous studies show that there is an obvious trade off between personalization and customer privacy. The main objective of this research is, therefore, to verify the impact of consumers’ information privacy concerns on firms’ collection and use of consumer information for web-based personalization where firms compete with different levels of ability in consumer information utilization for personalization. Our result shows that a firm of inferior ability in customer information utilization is more affected by privacy concerns than a firm of superior ability in choosing to collect and use consumer information for personalization. However, this does not mean that a firm of inferior ability, which chooses not to provide personalization due to privacy concerns, is worse off than a firm of superior ability, which chooses to provide personalization, in generating profits. On the contrary, a firm of superior ability can become worse off than a firm with inferior ability.     

Personalization–privacy paradox and consumer conflict with the use of location-based mobile commerce

This study empirically explored consumers’ response to the personalization–privacy paradox arising from the use of location-based mobile commerce (LBMC) and investigated the factors affecting consumers’ psychological and behavioral reactions to the paradox. A self-administered online consumer survey was conducted using a South Korean sample comprising those with experience using LBMC, and data from 517 respondents were analyzed. Using cluster analysis, consumers were categorized into four groups according to their responses regarding perceived personalization benefits and privacy risks: indifferent (n = 87), personalization oriented (n = 113), privacy oriented (n = 152), and ambivalent (n = 165). The results revealed significant differences across consumer groups in the antecedents and outcomes of the personalization–privacy paradox. Multiple regression analysis showed that factors influence the two outcome variables of the personalization–privacy paradox: internal conflict (psychological outcome) and continued use intention of LBMC (behavioral outcome). In conclusion, this study showed that consumer involvement, self-efficacy, and technology optimism significantly affected both outcome variables, whereas technology insecurity influenced internal conflict, and consumer trust influenced continued use intention. This study contributes to the current literature and provides practical implications for marketers and retailers aiming to succeed in the mobile commerce environment.     

AIoT-enabled smart surveillance for personal data digitalization: Contextual personalization-privacy paradox in smart home

Artificial intelligence of things technology provides smart surveillance capability for personal data digitalization. It will invade individuals’ information, physical, and social spaces and raise contextual privacy concerns while providing personalized services, which has not been explored in previous research. We theorize three types of smart surveillance and identify three subdimensions of contextual personalization and privacy concerns. Grounded in surveillance theory and personalization-privacy paradox, we examined the different trade-offs of contextual personalization and privacy concerns underlying the three types of smart surveillance on users’ behavioral intention in smart home context. The results also indicated that transparency can lessen the trade-off effects.