一种云计算的隐私类型阈值模型和隐私保护方法

云计算作为一种新的计算模式,可为用户提供低成本的动态计算资源和存储资源,能充分提高资源的使用效率.但是数据隐私安全问题已成为云计算应用普及的最大挑战.为了更好管理用户的隐私数据,在基于(n+1,t+1)阈值的基础上,提出了一种基于隐私类型的(n+1,t+1)阈值隐私保护模型,结合可信的第三方服务器隔离存储模型,用户可在授权后依据数据隐私类型采用基于角色的访问控制进行相应的访问操作.安全分析表明,基于隐私类型的保护机制在方便用户按类型访问云中的数据资源同时,可以充分地实现用户数据的分类隐私保护.     

分布式计算下基于差分隐私保护的参数估计

在大数据分析中,由于数据量巨大,储存于不同的机器中,常用的统计分析方法不能直接适用.因此需要对数据进行分布式计算.无论是分而治之还是多中心数据都需要对数据或计算中间结果进行传输.传输中不仅需要对数据进行隐私保护,也需要保证传输的高效性,同时传输次数过多不仅影响计算的效率,对数据的隐私保护也更有挑战.受此启发,本文在差分隐私模型下,提出了用于高效通讯的分布式参数估计算法中的隐私保护方案,并且严格证明了该方案既能有效保护数据安全,又不影响参数估计的有效性.最后,本文就线性模型下基于差分隐私保护算法的参数估计进行了模拟和实例验证.     

面向隐私保护的个性化搜索结果排序方法研究

针对个性化搜索中用户隐私保护的需求,提出一种面向隐私保护的个性化搜索结果排序方法.方法基于向量空间模型,引入安全点积计算方法来计算搜索相似度,旨在实现用户隐私保护的同时提高个性化搜索服务质量.首先分析了目前常用的搜索排序相似度计算方法存在的不足,然后分别介绍了向量空间模型和安全点积计算,最后详细介绍了面向隐私保护的个性化搜索结果排序方法,并对其性能进行了分析.     

基于安全多方的公平秘密共享方案

基于隐私保护的安全多方计算,提出一种新的公平秘密共享方案.方案利用隐私保护的安全多方计算,使参与者能够在不公开自己隐私秘密份额的情况下进行秘密恢复,从而确保了在所有参与者都诚实时,能恢复出真实秘密;当存在欺骗者时,都不能恢复出真实秘密,从而实现秘密恢复的公平性.与以往方案相比,该方案通过对私有秘密信息进行加密计算提高了安全性,同时在秘密恢复阶段,通过对秘密份额的安全多方计算,使其具有隐私保护功能.     

基于私有信息保护的线性回归安全算法

企业间进行合作时,经常涉及到对来自多个企业的数据信息进行分析.在某些情况下,由于这些数据涉及到各自的私有信息,如果不能保护这种私有信息,企业不愿意公开这些数据信息,但可能愿意提供这些相关数据的总体特征.基于这样的思路,针对企业数据的同构和异构两种不同分布方式,以及Paillier同态加密的两个主要性质,对合作企业的数据进行隐私计算,从而实现了企业间私有信息的合作线性回归分析.     

群组通信中密钥管理协议

为了提高群组通信中密钥管理协议的安全性和执行效率,分析了群组密钥中集中式密钥管理和分布式密钥管理,针对这两类协议的优势和不足,构造了一种群组密钥管理协议,此协议保留了集中式密钥管理中群组服务器,并融入了分布式管理协议的特点,吸取了两者的优点.最后我们对该协议的安全性和有效性进行了分析.结果表明,在安全性得到保证的前提下显著地提高了协议的执行效率.     

加法风险率模型下聚类的当前状态数据的回归分析（英文）

本文研究了加法风险率模型下聚类的当前状态数据(Ⅰ型区间删失数据)的回归分析问题.在相关的失效时间数据与簇类的规模有关的情形下,本文提出了一个簇内再抽样方法,并在一些正则条件下给出了相应估计量的极限分布理论.最后通过模拟实验验证了估计量的有限样本行为.     

隐私成本的模型研究

随着大数据时代的到来,数据的隐私、安全成本成为重要的研究课题.本文给出隐私定价模型的相关概念与问题描述,从价格和风险两方面探讨了隐私定价的相关参数和度量,包括隐私的内涵、内容和与价格相关的各种因素.隐私的定价机制应该包含选择合适的定价理论、定价模型,我们基于均衡价格理论,短期内个人信息价格是由供求均衡点决定,长期竞争均衡时价格等于长期平均成本的最低值,建立层次分析法、数学规划等数学模型方法解决隐私定价问题,并进行模拟计算.此外,对个人信息的市场供求关系、代际差异、政策法规与政府作用进行了讨论.     

基于模糊隶属函数无损可见数字水印

设计了一种基于模糊隶属函数的可见水印算法.该方法通过定义隶属度函数建立起和遮蔽图像相关的融合系数,每种像素的融合系数不相同,克服了单一融合系数易非法消除的问题.该方法可以无损恢复载体图像.其具有自恢复性,可以在限制非法用户使用图像的同时,为合法用户提供和原始图像完全一致的信息.通过对遮蔽子图像RH加密运用,将一位错扩散到整个图像中.非法用户在没有密钥的情况下,无法恢复出载体图像,安全性高,具有广阔的应用前景.     

基于迁移学习的GH159螺栓热镦后头部缺陷识别

为准确进行GH159螺栓热镦后头部缺陷识别,提出了基于迁移学习的缺陷识别方法,其中,不同场景亮度下的数据集分别设置为迁移学习的源域,目标域.首先,考虑域条件分布的多簇特点,使用K-means算法对同类缺陷数据进行簇划分,确定簇中心,并基于其构造新的分布差异度量;其次,为有效提升迁移学习计算效率,使用簇中心间距离以及各簇...     

SOM-based recommendations with privacy on multi-party vertically distributed data

Data collected for providing recommendations can be partitioned among different parties. Offering distributed data-based predictions is popular due to mutual advantages. It is almost impossible to present trustworthy referrals with decent accuracy from split data only. Meaningful outcomes can be drawn from adequate data. Those companies with distributed data might want to collaborate to produce accurate and dependable recommendations to their customers. However, they hesitate to work together or refuse to collaborate because of privacy, financial concerns, and legal issues. If privacy-preserving measures are provided, such data holders might decide to collaborate for better predictions. In this study, we investigate how to provide predictions based on vertically distributed data (VDD) among multiple parties without deeply jeopardizing their confidentiality. Users are first grouped into various clusters off-line using self-organizing map clustering while protecting the online vendors’ privacy. With privacy concerns, recommendations are produced based on partitioned data using a nearest neighbour prediction algorithm. We analyse our privacy-preserving scheme in terms of confidentiality and supplementary costs. Our analysis shows that our method offers recommendations without greatly exposing data holders’ privacy and causes negligible superfluous costs because of privacy concerns. To evaluate the scheme in terms of accuracy, we perform real-data-based experiments. Our experiment results demonstrate that the scheme is still able to provide truthful predictions.     

Some Basics on Privacy Techniques,Anonymization and their Big Data Challenges

With the progress in the information and communication fields, new opportunities and technologies for statistical analysis, knowledge discovery, data mining, and many other research areas have emerged, together with new challenges for privacy and data protection. Nowadays several personal records are kept in computerized databases. Personal data is collected and kept in census databases, medical databases, employee databases, among others. There has always been an asymmetry between the benefits of computerized databases and the rights of individual data subjects. Some data protection principles can be derived from the legal framework. In this survey, we present some basic cryptographic and non-cryptographic techniques that may be used for enhancing privacy, we focus mainly on anonymization in databases and networks, discuss some differences and interactions among the well-known models of k-anonymity and differential privacy and finally present some challenges to privacy that come from big data analytics.     

A key-lock-pair oriented access control scheme for the growth of users and files

A new access control scheme for the growth of users and files in file protection systems is proposed. Our scheme associates each user with a user key and each file with a file key. For each key, there are some corresponding locks, that can be extracted from a nonsingular matrix. Through simple operations on keys and locks, privacy decisions of the protection system can easily be revealed. Furthermore, by employing our method, whenever a new user or file is joined, the corresponding key values and lock values will be determined immediately without changing any previously defined keys and locks.     

Privacy preserving linear regression modeling of distributed databases

Statistical analysis is one of the important tools in data mining field. Little work has been conducted to investigate how statistical analysis could be performed when dataset are distributed among a number of data owners. Due to confidentiality or other proprietary reasons, data owners are reluctant to share data with others, while they wish to perform statistical analysis cooperatively. We address the important tradeoff between privacy and global statistical analysis such as linear regression, and present a privacy preserving linear regression model based on fully homomorphic encryption scheme.     

个性粒化k-匿名隐私保护方法研究

结合粒计算粗糙集理论和k-匿名理论的最新研究现状,从粒化角度出发,提出个性粒化k-匿名隐私保护方法.针对电子商务中隐私保护个性粒化决策选择度的不同,提出一种新的隐私保护方法:个性粒化k-匿名并给出了相关算法和定义,旨在实现更为合理的个性化隐私保护,提高隐私保护的精度.     

k-均值问题的差分隐私算法综述

$k$-均值问题是机器学习和组合优化领域十分重要的问题。它是经典的NP-难问题, 被广泛的应用于数据挖掘、企业生产决策、图像处理、生物医疗科技等领域。随着时代的发展, 人们越来越注重于个人的隐私保护:在决策通常由人工智能算法做出的情况下, 如何保证尽可能多地从数据中挖掘更多信息,同时不泄露个人隐私。近十年来不断有专家学者研究探索带隐私保护的$k$-均值问题, 得到了许多具有理论指导意义和实际应用价值的结果, 本文主要介绍关于$k$-均值问题的差分隐私算法供读者参考。     

Community structure-based trilateral stackelberg game model for privacy protection

Location-based services are widely used in mobile applications, which not only bring convenience, but also cause serious privacy concerns. Based on the characteristics of social network, this work proposes a cooperative protection architecture to model the relationship among users, communities and location-based service. Furthermore, in order to construct K anonymity set, a novel community structure-based trilateral Stackelberg game model is developed for K-anonymity protection. In addition, an optimization method based on the proposed model is designed by the backward induction process. Finally, the security and the performance under different situations such as the anonymity parameter K and the community structure parameter overlapping weights are analyzed. The analysis results indicate that the proposed model and the optimization method are effective for privacy protection and can achieve high secure in location-based services.     

An image encryption scheme based on quantum logistic map

The topic of quantum chaos has begun to draw increasing attention in recent years. While a satisfactory definition for it is not settled yet in order to differentiate between its classical counterparts. Dissipative quantum maps can be characterized by sensitive dependence on initial conditions, like classical maps. Considering this property, an implementation of image encryption scheme based on the quantum logistic map is proposed. The security and performance analysis of the proposed image encryption is performed using well-known methods. The results of the reliability analysis are encouraging and it can be concluded that, the proposed scheme is efficient and secure. The results of this study also suggest application of other quantum maps such as quantum standard map and quantum baker map in cryptography and other aspects of security and privacy.     

车载自组网下分布式身份认证机制

针对车载自组网中存在的安全隐患,提出一种身份认证机制.将门限RSA签名技术应用到车载自组网中,从而保证了车载自组网中节点通信信息的保密性、真实性、完整性及不可否认性.实验结果表明,在成员数量和门限值不断变换下,分发时间和合成时间变换比较稳定,说明此模型是稳定性较好的模型,所以将门限RSA签名技术应用于保证车载自组网节点通信的信息安全性是正确可行的.