Privacy self-correlation privacy-preserving scheme in LBS

The prevalence of mobile intelligent terminals gives the location-based service (LBS) more opportunities to enrich mobile users’ lives.However,mobile users enjoy the convenience with the cost of personal privacy.The side information and mobile user’s recent requirement records were considered,which were obtained or stored by the service provider.Based on the existence of recent requirement records,adversary can employ the inference attack to analysis mobile user’s personal information.Therefore,two schemes were proposed,including of basic privacy self-correlation privacy-preserving scheme (Ba-2PS) and enhanced privacy self-correlation privacy-preserving scheme(En-2PS).In En-2PS,the privacy-preserving scheme was designed from two dimensions of aspects of time factor and query region,which increased the uncertainty inferring out the real information.Finally,the privacy analysis was illustrated to proof En-2PS’s privacy degree,then the performance and privacy evaluation results indicate that En-2PS is effective and efficient.     

情境感知的位置隐私保护方法研究进展

随着无线通信技术与智能移动终端的发展,基于位置的服务(LBS, location-based service)得到广泛应用,与移动对象位置相关的数据隐私保护已经成为LBS中的研究热点。首先简单介绍了位置隐私与情境感知的基本概念;其次,对现有的位置隐私保护方法从隐私保护效果、服务质量、系统结构和时空情境自适应性4个方面进行分析总结,指出了该研究的发展趋势;最后,对情境感知的位置隐私保护方法现状进行介绍,讨论了该领域存在的研究难点以及未来的研究方向。     

移动互联网中的位置隐私保护研究

全面归纳了移动互联网中位置隐私保护的相关研究工作,总结了位置服务和定位服务中的威胁模型。然后,详细介绍了现有基于位置服务的隐私保护技术,分析了其在抗隐私攻击和位置隐私适用性方面的优缺点,并阐述了定位服务中位置隐私问题的本质、威胁和解决方法。最后指出了需要进一步研究的问题。     

基于服务相似性的k-匿名位置隐私保护方法

针对当前基于位置的服务(LBS)系统存在的隐私保护度、位置服务质量和通信开销三者难于平衡的问题,提出了一种基于服务相似性的k-匿名位置隐私保护方法。在不改变现有LBS 系统架构的情况下,利用位置服务查询结果的相似性来辅助匿名服务器构造匿名区域,从而实现在确保用户隐私安全的基础上,有效提高服务质量和降低系统开销。最后,通过实验验证了该算法的有效性。     

位置服务信息安全防护

随着无线通信技术和智能移动终端的快速发展,基于位置的服务(LBS)在军事、交通、物流等诸多领域得到了广泛应用,它能够根据移动对象的位置信息提供个性化服务。在人们享受各种位置服务的同时,移动对象个人信息泄露的隐私威胁也渐渐成为一个严重的问题。为移动用户提供位置服务的同时,保护移动用户的位置隐私也至关重要。本文就位置业务隐私保护技术和位置业务隐私管控手段进行了探讨。     

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.     

一种基于差分隐私和时序的推荐系统模型研究

推荐系统的建立依赖用户的个人隐私信息,攻击者可以通过推荐的结果对用户的状态和行为进行预测.目前,虽然有对基于协同过滤近邻隐私保护的研究,但是对基于模型的隐私保护的关注度并不够高.差分隐私理论定义了一个相当严格的防攻击模型,通过添加噪声使数据失真达到隐私保护的目的,而且用户的兴趣存在兴趣漂移问题,对推荐效果造成影响,因此,提出基于差分隐私理论和时序理论构建基于模型的推荐系统.首先,根据差分隐私理论,给用户的评分数据增加小波动的符合Laplace分布的噪声,增大待分解矩阵的安全系数;然后,在随机梯度下降模型的基础上,将时序因子建模为时间权重,提高模型的准确性.实验证明该算法的准确性,并且为增强隐私研究提供了新的思路.     

基于位置语义的路网位置隐私保护

移动用户在享受基于位置的服务（LBS）的同时受到位置隐私泄露的威胁,因而提供有效的位置隐私保护策略至关重要。传统的位置隐私保护方法主要采用空间匿名的方式,若攻击者获得了更多与匿名空间相关的背景知识,尤其是与位置相关的语义信息,就会严重降低匿名效果。为了防止由位置语义分析造成的敏感位置信息泄露,并根据移动用户活动范围大多限定为道路网络的特点,提出一种基于位置语义的路网位置隐私保护方法,充分考虑了用户的个性化隐私需求,并通过实验验证了方法的可行性及有效性。     

Privacy preservation in wireless sensor networks: A state-of-the-art survey

Much of the existing work on wireless sensor networks (WSNs) has focused on addressing the power and computational resource constraints of WSNs by the design of specific routing, MAC, and cross-layer protocols. Recently, there have been heightened privacy concerns over the data collected by and transmitted through WSNs. The wireless transmission required by a WSN, and the self-organizing nature of its architecture, makes privacy protection for WSNs an especially challenging problem. This paper provides a state-of-the-art survey of privacy-preserving techniques for WSNs. In particular, we review two main categories of privacy-preserving techniques for protecting two types of private information, data-oriented and context-oriented privacy, respectively. We also discuss a number of important open challenges for future research. Our hope is that this paper sheds some light on a fruitful direction of future research for privacy preservation in WSNs.     

大规模无线通信网络移动边缘计算和缓存研究

面向未来6G移动通信的大规模网络移动边缘计算与缓存技术,首先,介绍了大规模无线网络下移动边缘计算和缓存的架构与原理,并阐释了移动边缘计算和缓存技术在大规模无线网络中的必要性和普适性。接着,从计算卸载、边缘缓存、多维资源分配、用户关联和隐私保护这5个关键问题出发,综述和分析了移动边缘计算和缓存赋能大规模无线网络时会引入的新型关键问题以及对应的解决方案研究,并进一步指出了未来的发展趋势和研究方向。最后,针对隐私保护问题,提出了一种基于联邦学习的隐私保护方案,并通过仿真结果表明所提方案能够同时保护用户数据隐私且改善系统服务质量。     

State-of-the-art survey of privacy-preserving data aggregation in wireless sensor networks

A state-of-the-art survey of privacy-preserving data aggregation techniques in wireless sensor networks was reviewed.Firstly,preliminaries were introduced,including network models,adversary models,and performance evaluation metrics.Secondly,existing related work was classified into several types according to privacy preservation techniques,such as homomorphic encryption,data perturbation,slicing-mixing technique,generalization,secure multiparty computation,and the key mechanisms of typical protocols were elaborated and analyzed.Finally,the promising future research directions were discussed.     

Trajectory privacy protection method based on location obfuscation

In the process of continuous queries,a method of trajectory privacy protection based on location obfuscation was proposed to solve the problem that K-anonymity was difficult to guarantee user privacy in third party architectrue.Firstly,the (G-1) query obfuscation locations through the location prediction was obtained and the dummy location selection mechanism,and then sent them together with the user’s real query location to different anonymizers to form cloaking regions and sent them to the LBS server for queries,and the query results were returned to the user by different anonymizers.In this method,the user’s real query location was confused by the location obfuscation,and the attacker couldn’t deduce the user’s trajectory from a single anonymizer or the LBS server.The method can enhance the privacy of the user’s trajectory and can effectively solve the performance bottleneck in the single anonymizer structure.Security analysis shows the security of the proposed approach,and experiments show this method can reduce the number of interactions between the user and the LBS server and the overhead of the single anonymizer.     

User location privacy protection mechanism for location-based services

With the rapid development of the Internet of Things (IoT), Location-Based Services (LBS) are becoming more and more popular. However, for the users being served, how to protect their location privacy has become a growing concern. This has led to great difficulty in establishing trust between the users and the service providers, hindering the development of LBS for more comprehensive functions. In this paper, we first establish a strong identity verification mechanism to ensure the authentication security of the system and then design a new location privacy protection mechanism based on the privacy proximity test problem. This mechanism not only guarantees the confidentiality of the user’s information during the subsequent information interaction and dynamic data transmission, but also meets the service provider’s requirements for related data.     

云计算环境中支持隐私保护的数字版权保护方案简

针对云计算环境中数字内容安全和用户隐私保护的需求,提出了一种云计算环境中支持隐私保护的数字版权保护方案。设计了云计算环境中数字内容版权全生命周期保护和用户隐私保护的框架,包括系统初始化、内容加密、许可授权和内容解密4个主要协议;采用基于属性基加密和加法同态加密算法的内容加密密钥保护和分发机制,保证内容加密密钥的安全性;允许用户匿名向云服务提供商订购内容和申请授权,保护用户的隐私,并且防止云服务提供商、授权服务器和密钥服务器等收集用户使用习惯等敏感信息。与现有的云计算环境中数字版权保护方案相比,该方案在保护内容安全和用户隐私的同时,支持灵活的访问控制,并且支持在线和超级分发应用模式,在云计算环境中具有较好的实用性。     

Achieving efficient location privacy protection based on cache

With the development of mobile Internet and the popularization of intelligent terminals,location based services(LBS) has been widespread in navigation,social network and other fields.Due to the sensitivity of personal location information,data privacy protection related to location of mobile objects has become the hotspots of research.Considering the openness of the background information,and based on the information cache mechanism,a dummy selection algorithm was proposed to reduce the number of interactions between the user and the untrusted server and combine the k-anonymity to achieve efficient location privacy.Without relying on trusted third party,the scheme can prevent the attackers owned background information from inference attack,and the detail simulation results indicate its effectiveness and efficiency.     

隐私计算研究范畴及发展趋势

随着移动互联网、云计算和大数据技术的广泛应用,电商、搜索、社交网络等服务在提供便利的同时,大数据分析使用户隐私泄露的威胁日益凸显,不同系统隐私保护策略和能力的差异性使隐私的延伸管理更加困难,同一信息的隐私保护需求随时间变化需要多种隐私保护方案的组合协同。目前已有的各类隐私保护方案大多针对单一场景,隐私缺乏定量化的定义,隐私保护的效果、隐私泄露的利益损失以及隐私保护方案融合的复杂性三者之间的关系刻画缺乏系统的计算模型。因此,在分析隐私保护研究现状的基础上,提出隐私计算的概念,对隐私计算的内涵加以界定,从隐私信息的全生命周期讨论隐私计算研究范畴,并从隐私计算模型、隐私保护场景适应的密码理论、隐私控制与抗大数据分析的隐私保护、基于信息隐藏的隐私保护以及支持高并发的隐私保护服务架构等方面展望隐私计算的发展趋势。     

CP-ABE based users collaborative privacy protection scheme for continuous query

In location-based services (LBS),as the untrusted LBS server can be seen as an adversary,and it can utilize the attribute as background knowledge to correlate the real location of the user in the set of uncertain locations.Then the adversary can gain the location privacy when the user enjoys the snapshot and continuous query through the correlation inference attack.In order to cope with this attack,the main scheme in privacy protection is to generalize the attribute and achieve attribute anonymity.However,algorithms of this type usually assumes a trusted third party (TTP) which provides the service of similarity attribute finding and comparing,and it is unpractical in the real environment,as the TTP may become the point of attack or the bottleneck of service and it cannot be considered as the trusted one all the time.Thus,to cope with the correlation inference attack as well as the semi-trusted third party,ciphertext policy attribute based encryption (CP-ABE) and users collaboration based attribute anonymous scheme was proposed.In this scheme,the user coupled achieve location and attribute anonymity.Furthermore,this scheme could also provide security for attacks from the semi-trusted third party as well as semi-trusted collaborative users.At last,security analysis and the experiment results further verify the effectiveness of privacy protection and the efficiency of algorithm execution.     

Research on LBS privacy preservation based on pseudorandom permutation in road network

A method of privacy preservation based on pseudorandom permutation was put forward for the issues of location privacy and query content privacy.Firstly,the distribution information of points of interest (PoI) based on the vertexes in the road network was organized,each single road vertex was taken as the foundational processing object.Based on the pseudorandom permutation,a permutation scheme of the point-of-interest records at the LBS server's end was put forward,a 32-bit random seed was adopted to generate a permuted table in the scheme,and the point-of-interest records were encrypted and permuted according to the table.These processed records were stored in the LBS database.Then a trusted intermediate server,replacing of the user,issued a query request with a record number instead of the query content to the LBS server.The LBS server could not determine which kind of PoI the user was interested in or which road section the user was locating on,and therefore the scheme achieved private information retrieval.Finally,the efficiency in the metrics of query accuracy,communication overhead and processing time was also analyzed.By the performance analysis and extensive experiments,the proposed scheme is proved to be location untraceable and query content uncorrelation.     

基于代理的移动互联网隐私保护机制

国家自然科学基金资助项目（61170251）;国家自然科学基金委—广东联合基金资助项目（U1401251）;国家高技术研究发展计划（“863”计划）基金资助项目（2012AA013102）;教育部重点基金资助项目（209156）     

Privacy-preserving photo sharing framework cross different social network

With rapid developments of digital photography and social networks,users of photo-sharing-supported social networking applications can easily forward photos across different social networks at the cost of their growing privacy concerns.To address this problem,a privacy-preserving photo sharing framework was proposed,which could apply to extended control and privacy invasion tracing.In extended control scheme,the following users on a dissemination chain was restrained by each user’s privacy policy.Then several privacy areas of photos were encrypted and the access control polices were bound to the uploaded photos,so that any privacy areas on the photos could be hidden away from unwanted viewers even across different social networks.On this basis,the behaviors of users were record by tracing scheme of privacy invasion,the integrality of records was protected by using nested signature algorithm.The correctness,security and performance of overhead of the scheme are then thoroughly analyzed and evaluated via detailed simulations.