基于0-1编码的参与式感知隐私保护的数据价值匹配方案

在参与式感知中,满足数据请求者对数据类型和数据价值匹配的要求,同时保护请求者和提供者的个人隐私,是普及参与式感知需要解决的问题。鉴于此,提出了一种基于0-1编码的隐私保护的数据价值匹配方案,它将用户数据价值转换成0-1编码,然后使用时空高效的布隆过滤器执行价值匹配,在保护了用户数据价值隐私的同时,完成了数据价值的高效匹配。理论分析和仿真实验论证了所提方案的正确性、安全性和高效性。     

Equally contributory privacy-preserving k-means clustering over vertically partitioned data

In recent years, there have been numerous attempts to extend the k-means clustering protocol for single database to a distributed multiple database setting and meanwhile keep privacy of each data site. Current solutions for (whether two or more) multiparty k-means clustering, built on one or more secure two-party computation algorithms, are not equally contributory, in other words, each party does not equally contribute to k-means clustering. This may lead a perfidious attack where a party who learns the outcome prior to other parties tells a lie of the outcome to other parties. In this paper, we present an equally contributory multiparty k-means clustering protocol for vertically partitioned data, in which each party equally contributes to k-means clustering. Our protocol is built on ElGamal's encryption scheme, Jakobsson and Juels's plaintext equivalence test protocol, and mix networks, and protects privacy in terms that each iteration of k-means clustering can be performed without revealing the intermediate values.     

A classification-based privacy-preserving decision-making for secure data sharing in Internet of Things assisted applications

The introduction of the Internet of Things (IoT) paradigm serves as pervasive resource access and sharing platform for different real-time applications. Decentralized resource availability, access, and allocation provide a better quality of user experience regardless of the application type and scenario. However, privacy remains an open issue in this ubiquitous sharing platform due to massive and replicated data availability. In this paper, privacy-preserving decision-making for the data-sharing scheme is introduced. This scheme is responsible for improving the security in data sharing without the impact of replicated resources on communicating users. In this scheme, classification learning is used for identifying replicas and accessing granted resources independently. Based on the trust score of the available resources, this classification is recurrently performed to improve the reliability of information sharing. The user-level decisions for information sharing and access are made using the classification of the resources at the time of availability. This proposed scheme is verified using the metrics access delay, success ratio, computation complexity, and sharing loss.     

卷积神经网络中具有隐私保护属性的预测分类算法

在用户将其数据上传到公共平台并利用卷积神经网络进行相关操作以得到运算结果的过程中如何保证用户敏感数据的私密性就成为了一个亟待解决的问题.对此采用密码学中的同态加密算法对用户输入数据进行加密处理,在已训练过的卷积神经网络模型中对加密的数据进行预测分类,并得到最终的分类结果,输出的结果是加密形式的,为进一步保护数据的机密性...     

一种带完整性验证的数据聚集隐私保护算法

为使无线传感器网络可以真正满足大规模应用的需求,提出了一种既能保护数据隐私又能验证数据完整性的聚集算法。算法首先构造不相交聚集树,然后让节点在各自对应的时间片内,按不同度数将自身数据分解为数个切片,并将切片分别加密传输至各聚集树中,达到保护节点数据隐私和获取冗余数据的目的,最后采用基于路由树的网内聚集将各聚集树的聚集结果传送至基站,由基站验证最终结果的完整性。仿真实验表明,在资源受限特征突出的无线传感器网络中,算法能够以较低的通信开销获得较高准确度的聚集结果,并具备较好的隐私保护性能和鉴别聚集结果完整性的能力。     

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。     

基于博弈论的隐私保护分布式数据挖掘

隐私保护的分布式数据挖掘问题是数据挖掘领域的一个研究热点,而基于经济视角,利用博弈论的方法对隐私保护分布式数据挖掘进行研究只是处于初始阶段。基于收益最大化,研究了完全信息静态博弈下分布式数据挖掘中参与者(两方或多方)的策略决策问题,得出了如下结论:数据挖掘在满足一定的条件下,参与者(两方或多方)的准诚信攻击策略是一个帕累托最优的纳什均衡策略;在准诚信攻击的假设下,参与者(多方)的非共谋策略并不是一个纳什均衡策略。同时给出了该博弈的混合战略纳什均衡,它对隐私保护分布式数据挖掘中参与者的决策具有一定的理论和指导意义。     

A distributed approach to enabling privacy-preserving model-based classifier training

This paper proposes a novel approach for privacy-preserving distributed model-based classifier training. Our approach is an important step towards supporting customizable privacy modeling and protection. It consists of three major steps. First, each data site independently learns a weak concept model (i.e., local classifier) for a given data pattern or concept by using its own training samples. An adaptive EM algorithm is proposed to select the model structure and estimate the model parameters simultaneously. The second step deals with combined classifier training by integrating the weak concept models that are shared from multiple data sites. To reduce the data transmission costs and the potential privacy breaches, only the weak concept models are sent to the central site and synthetic samples are directly generated from these shared weak concept models at the central site. Both the shared weak concept models and the synthetic samples are then incorporated to learn a reliable and complete global concept model. A computational approach is developed to automatically achieve a good trade off between the privacy disclosure risk, the sharing benefit and the data utility. The third step deals with validating the combined classifier by distributing the global concept model to all these data sites in the collaboration network while at the same time limiting the potential privacy breaches. Our approach has been validated through extensive experiments carried out on four UCI machine learning data sets and two image data sets.