Visual privacy-preserving level evaluation for multilayer compressed sensing model using contrast and salient structural features

Recognition and classification tasks in images or videos are ubiquitous, but they can lead to privacy issues. People increasingly hope that camera systems can record and recognize important events and objects, such as real-time recording of traffic conditions and accident scenes, elderly fall detection, and in-home monitoring. However, people also want to ensure these activities do not violate the privacy of users or others. The sparse representation classification and recognition algorithms based on compressed sensing (CS) are robust at recognizing human faces from frontal views with varying expressions and illuminations, as well as occlusions and disguises. This is a potential way to perform recognition tasks while preserving visual privacy. In this paper, an improved Gaussian random measurement matrix is adopted in the proposed multilayer CS (MCS) model to realize multiple image CS and achieve a balance between visual privacy-preserving and recognition tasks. The visual privacy-preserving level evaluation for MCS images has important guiding significance for image processing and recognition. Therefore, we propose an image visual privacy-preserving level evaluation method for the MCS model (MCS-VPLE) based on contrast and salient structural features. The basic concept is to use the contrast measurement model based on the statistical mean of the asymmetric alpha-trimmed filter and the salient generalized center-symmetric local binary pattern operator to extract contrast and salient structural features, respectively. The features are fed into a support vector regression to obtain the image quality score, and the fuzzy c-means algorithm is used for clustering to obtain the final evaluated image visual privacy-preserving score. Experiments on three constructed databases show that the proposed method has better prediction effectiveness and performance than conventional methods.     

Privacy-preserving real-time road conditions monitoring scheme based on intelligent traffic

To alleviate the traffic pressure on roads,reduce the appearance of road congestion,and avoid the occurrence of traffic accidents,a privacy-preserving intelligent monitoring (PPIM) scheme based on intelligent traffic was proposed in combination with the safe and k-nearest neighbor (KNN) algorithm.To ensure the security of traffic data,the data content was randomly divided into independent parts via the secure multi-party computing strategy,and the data components were stored and encrypted separately by non-colluding multi-servers.To improve the accuracy of road condition monitoring,an improved KNN traffic monitoring algorithm was proposed.By virtue of the similarity calculation of data,the correlation value to measure the degree of traffic condition relationship between roads was obtained.And it was integrated with the KNN as the weight coefficient.To speed up the processing of dense data,a series of data security computing protocols were designed,and the data security processing was realized.In addition,real traffic data were used to verify the algorithm.The results show that the improved KNN algorithm is helpful to improve the accuracy of traffic monitoring.The analysis shows that the algorithm can not only guarantee the safety of data but improve the accuracy of traffic monitoring.     

基于非负矩阵分解的隐私保护协同过滤算法

提出一种基于非负矩阵分解的隐私保护协同过滤推荐算法．该算法在用户数据收集过程中采用随机扰动技术,并使用非负矩阵分解对数据进行处理,从而形成隐私保护功能,并在此基础上产生推荐．理论分析和实验结果表明,该算法在保护用户个人隐私的基础上,能够产生具有一定精确性的推荐结果.     

恶意模型下保密点积协议的设计与分析*

保密点积协议是许多安全多方计算问题中一个重要的协议,常被用在许多保密数据挖掘协议中,为这些协议提供了重要的安全保证。目前,一些已存在的保密点积协议至多在半诚实模型下是安全的。基于一些基本的密码学技术设计了一个恶意模型下安全的保密两方共享点积协议,这个协议比以往协议具有更高的安全性。该协议潜在的应用领域是广阔的,如计算Euclidean距离、保密计算几何、保密协作统计分析等。     

隐私保护的时序规则分布挖掘

针对多方分别拥有一部分数据记录,并想在不泄露自己隐私数据的情况下联合对各自的时序序列进行分割的问题,提出基于半可信第三方的隐私保护的时序规则分布挖掘方法。将联合计算时序规则各频度的问题转化成多方秘密比较数大小的问题,从而设计一个简单的基于半可信第三方的算法,解决多方联合计算时序规则频度的问题。     

DPPS: A novel dual privacy-preserving scheme for enhancing query privacy in continuous location-based services

Since smartphones embedded with positioning systems and digital maps are widely used, location-based services (LBSs) are rapidly growing in popularity and providing unprecedented convenience in people’s daily lives; however, they also cause great concern about privacy leakage. In particular, location queries can be used to infer users’ sensitive private information, such as home addresses, places of work and appointment locations. Hence, many schemes providing query anonymity have been proposed, but they typically ignore the fact that an adversary can infer real locations from the correlations between consecutive locations in a continuous LBS. To address this challenge, a novel dual privacy-preserving scheme (DPPS) is proposed that includes two privacy protection mechanisms. First, to prevent privacy disclosure caused by correlations between locations, a correlation model is proposed based on a hidden Markov model (HMM) to simulate users’ mobility and the adversary’s prediction probability. Second, to provide query probability anonymity of each single location, an advanced k-anonymity algorithm is proposed to construct cloaking regions, in which realistic and indistinguishable dummy locations are generated. To validate the effectiveness and efficiency of DPPS, theoretical analysis and experimental verification are further performed on a real-life dataset published by Microsoft, i.e., GeoLife dataset.     

机器学习隐私保护研究综述

机器学习已成为大数据、物联网和云计算等领域的核心技术.机器学习模型训练需要大量数据,这些数据通常通过众包方式收集,其中含有大量隐私数据,包括个人身份信息(如电话号码、身份证号等)、敏感信息(如金融财务、医疗健康等信息).如何低成本且高效地保护这些数据是一个重要的问题.介绍了机器学习及其隐私定义和隐私威胁,重点对机器学习隐私保护主流技术的工作原理和突出特点进行了阐述,并分别按照差分隐私、同态加密和安全多方计算等机制对机器学习隐私保护领域的研究成果进行了综述.在此基础上,对比分析了机器学习不同隐私保护机制的主要优缺点.最后,对机器学习隐私保护的发展趋势进行展望,并提出该领域未来可能的研究方向.     

可追溯的广义指定验证者签名证明方案

为了解决传统广义指定验证者签名证明方案中强隐私保护性质对验证者不公平的问题,提出了可追溯的广义指定验证者签名证明（traceable universal designated verifier signature proof,TUDVSP）方案.在TUDVSP方案中,引入一个追溯中心,可将指定者的转换签名恢复为原始签名,从而防止签名者与指定者合谋欺骗验证者.基于现实应用考虑,从不可伪造性、抗仿冒攻击和可追溯性这3个方面定义了TUDVSP方案的安全模型.利用双线性映射构造具体的TUDVSP方案,并证明该方案具有不可伪造性、抗仿冒攻击和可追溯性.实验结果表明,完成一次签名追溯仅需21 ms左右的计算开销与120字节的通信开销.     

Privacy-preserving multi-attribute reverse spectrum auction scheme

Aiming the problem that the existing reverse spectrum auctions do not take the non-price attribute of spectrum into account and the security of spectrum auction,a privacy-preserving multi-attribute reverse spectrum auction was proposed.Firstly,price and non-price positive attributes of spectrum were considered as the bidding scheme of bidders and auctions was performed to judge spectrum winners.Secondly,to ensure the security of the spectrum auction,the Paillier threshold system was used to introduce a group of spectrum auction servers of auctioneers to replace the traditional single third-party agency,which could prevent the fraud collusion between spectrum auctioneers and bidders.The cryptography tools such as the anonymization technology and oblivious transfer were introduced to achieve the secure features,which could make the spectrum auction performed securely.The security analysis shows that the security protocol has strong security.The performance of the protocol is also evaluated,and experimental results show that the security scheme is superior to the multi-attribute reverse auction security scheme that can be applied in the spectrum auction scenario in terms of computational overhead.     

无线传感器网络中安全高效的空间数据聚集算法

提出了一种传感器网络中安全高效的空间数据聚集算法SESDA（secure and energy-efficient spatial dataaggregation algorithm）.SESDA 基于路线方法实现数据聚集,由于算法沿着已设计好的路线执行聚集请求和数据聚集,使得SESDA 不受网络拓扑结构的影响,适用于网络拓扑结构动态变化的传感器网络,且节省了网络拓扑结构的维护消耗.此外,针对过多加/解密操作对节点能量急剧消耗的特点,SESDA 通过安全通道传输感知数据来保证数据的隐私性,避免了节点之间在数据传输过程中需要对感知数据进行加/解密操作,不仅可以节约节点大量的能量从而延长网络寿命,而且使得数据聚集具有很小的处理延迟,因而获得较高的聚集精确度.理论分析和实验结果显示,SESDA 具有低通信量、低能耗、高安全性和高精确度的特点.