Multi-Source Data Privacy Protection Method Based on Homomorphic Encryption and Blockchain

Multi-Source data plays an important role in the evolution of media convergence.Its fusion processing enables the further mining of data and utilization of data value and broadens the path for the sharing and dissemination of media data.However,it also faces serious problems in terms of protecting user and data privacy.Many privacy protectionmethods have been proposed to solve the problemof privacy leakage during the process of data sharing,but they suffer fromtwo flaws:1)the lack of algorithmic frameworks for specific scenarios such as dynamic datasets in the media domain;2)the inability to solve the problem of the high computational complexity of ciphertext in multi-source data privacy protection,resulting in long encryption and decryption times.In this paper,we propose a multi-source data privacy protection method based on homomorphic encryption and blockchain technology,which solves the privacy protection problem ofmulti-source heterogeneous data in the dissemination ofmedia and reduces ciphertext processing time.We deployed the proposedmethod on theHyperledger platformfor testing and compared it with the privacy protection schemes based on k-anonymity and differential privacy.The experimental results showthat the key generation,encryption,and decryption times of the proposedmethod are lower than those in data privacy protection methods based on k-anonymity technology and differential privacy technology.This significantly reduces the processing time ofmulti-source data,which gives it potential for use in many applications.

Redundant Data Detection and Deletion to Meet Privacy Protection Requirements in Blockchain-Based Edge Computing Environment

With the rapid development of information technology,IoT devices play a huge role in physiological health data detection.The exponential growth of medical data requires us to reasonably allocate storage space for cloud servers and edge nodes.The storage capacity of edge nodes close to users is limited.We should store hotspot data in edge nodes as much as possible,so as to ensure response timeliness and access hit rate;However,the current scheme cannot guarantee that every sub-message in a complete data stored by the edge node meets the requirements of hot data;How to complete the detection and deletion of redundant data in edge nodes under the premise of protecting user privacy and data dynamic integrity has become a challenging problem.Our paper proposes a redundant data detection method that meets the privacy protection requirements.By scanning the cipher text,it is determined whether each sub-message of the data in the edge node meets the requirements of the hot data.It has the same effect as zero-knowledge proof,and it will not reveal the privacy of users.In addition,for redundant sub-data that does not meet the requirements of hot data,our paper proposes a redundant data deletion scheme that meets the dynamic integrity of the data.We use Content Extraction Signature(CES)to generate the remaining hot data signature after the redundant data is deleted.The feasibility of the scheme is proved through safety analysis and efficiency analysis.

VKFQ:A Verifiable Keyword Frequency Query Framework with Local Differential Privacy in Blockchain

With its untameable and traceable properties,blockchain technology has been widely used in the field of data sharing.How to preserve individual privacy while enabling efficient data queries is one of the primary issues with secure data sharing.In this paper,we study verifiable keyword frequency(KF)queries with local differential privacy in blockchain.Both the numerical and the keyword attributes are present in data objects;the latter are sensitive and require privacy protection.However,prior studies in blockchain have the problem of trilemma in privacy protection and are unable to handle KF queries.We propose an efficient framework that protects data owners’privacy on keyword attributes while enabling quick and verifiable query processing for KF queries.The framework computes an estimate of a keyword’s frequency and is efficient in query time and verification object(VO)size.A utility-optimized local differential privacy technique is used for privacy protection.The data owner adds noise locally into data based on local differential privacy so that the attacker cannot infer the owner of the keywords while keeping the difference in the probability distribution of the KF within the privacy budget.We propose the VB-cm tree as the authenticated data structure(ADS).The VB-cm tree combines the Verkle tree and the Count-Min sketch(CM-sketch)to lower the VO size and query time.The VB-cm tree uses the vector commitment to verify the query results.The fixed-size CM-sketch,which summarizes the frequency of multiple keywords,is used to estimate the KF via hashing operations.We conduct an extensive evaluation of the proposed framework.The experimental results show that compared to theMerkle B+tree,the query time is reduced by 52.38%,and the VO size is reduced by more than one order of magnitude.

An intelligent and privacy-enhanced data sharing strategy for blockchain-empowered Internet of Things

With the development of the Internet of Things(IoT),the massive data sharing between IoT devices improves the Quality of Service(QoS)and user experience in various IoT applications.However,data sharing may cause serious privacy leakages to data providers.To address this problem,in this study,data sharing is realized through model sharing,based on which a secure data sharing mechanism,called BP2P-FL,is proposed using peer-to-peer federated learning with the privacy protection of data providers.In addition,by introducing the blockchain to the data sharing,every training process is recorded to ensure that data providers offer high-quality data.For further privacy protection,the differential privacy technology is used to disturb the global data sharing model.The experimental results show that BP2P-FL has high accuracy and feasibility in the data sharing of various IoT applications.

Application and Challenge of Blockchain Technology in Medical Field

Due to its unique security,blockchain technology is widely used in the financial field.Under the background of the rapid development of information technology and the rapid improvement of medical level,it is also a general trend to integrate blockchain technology into the medical field.According to the characteristics of blockchain and the research contents of many scholars on the application of blockchain in the medical field,this paper analyzes and summarizes the problems existing in the current development of blockchain,puts forward corresponding solutions,and looks forward to the further application of blockchain technology in the medical field.

Blockchain data secure sharing protocol based on threshold Paillier algorithm

With the development of Internet technology,secure storage and secure sharing of data have become increasingly important.Traditional data sharing schemes exist a series of problems including lack of security and low efficiency.In this paper,we construct a secure and efficient data sharing scheme based on threshold Paillier algorithm and blockchain technology,which achieves secure data storage and sharing without a third-party institution.Firstly,we propose a(t,l)threshold Paillier blockchain data sharing scheme,which effectively prevents decryption failures caused by the loss of a single node’s private key.Secondly,we propose a combined on-chain and off-chain data storage scheme,we store the ciphertext on the cloud server and the ciphertext hash value on the blockchain,which not only ensures the integrity of the data but also solves the storage limitation problem on the blockchain.Finally,we use the simulation paradigm to prove the security of the scheme in the semi-honest model.The discussion results of the comparison and the analysis of performance show that the blockchain data security sharing scheme proposed in this paper has lower computational overhead and higher security than other similar schemes.

A Cover-Independent Deep Image Hiding Method Based on Domain Attention Mechanism

Recently,deep image-hiding techniques have attracted considerable attention in covert communication and high-capacity information hiding.However,these approaches have some limitations.For example,a cover image lacks self-adaptability,information leakage,or weak concealment.To address these issues,this study proposes a universal and adaptable image-hiding method.First,a domain attention mechanism is designed by combining the Atrous convolution,which makes better use of the relationship between the secret image domain and the cover image domain.Second,to improve perceived human similarity,perceptual loss is incorporated into the training process.The experimental results are promising,with the proposed method achieving an average pixel discrepancy(APD)of 1.83 and a peak signal-to-noise ratio(PSNR)value of 40.72 dB between the cover and stego images,indicative of its high-quality output.Furthermore,the structural similarity index measure(SSIM)reaches 0.985 while the learned perceptual image patch similarity(LPIPS)remarkably registers at 0.0001.Moreover,self-testing and cross-experiments demonstrate the model’s adaptability and generalization in unknown hidden spaces,making it suitable for diverse computer vision tasks.

A systematic literature review of the tension between the GDPR and public blockchain systems

Blockchain technology has been rapidly growing since Bitcoin was invented in 2008.The most common type of blockchain system,public(permissionless)blockchain system,has some unique features that lead to a tension with the European Union’s General Data Protection Regulation(GDPR)and other similar data protection laws.In this paper,we report the results of a systematic literature review(SLR)on 114 research papers discussing and/or addressing such a tension.To the best of our knowledge,our SLR is the most comprehensive review of this tension,leading to a more in-depth and broader analysis of related research work on this important topic.Our results revealed three main types of issues:(i)difficulties in exercising data subjects’rights such as the‘right to be forgotten’(RTBF)due to the immutable nature of public blockchains;(ii)difficulties in identifying roles and responsibilities in the public blockchain data processing ecosystem(particularly on the identification of data controllers and data processors);and(iii)ambiguities regarding the application of the relevant law(s)due to the distributed nature of blockchains.Our work also led to a better understanding of solutions for improving the GDPR compliance of public blockchain systems.It can help inform not only blockchain researchers and developers but also policymakers and law markers to consider how to reconcile the tension between public blockchain systems and data protection laws(the GDPR and beyond).

基于全同态加密优化的云数据隐私保护方法

为了提升云数据的安全性,改善数据加密时间长以及加密效果较差等问题,提出一种基于全同态加密优化的云数据隐私保护方法。通过生成对抗网络模型学习原始数据中的重要特征,确保合成的数据和初始数据之间具有较高的相似度,使其能够满足差分隐私特征。引入PKI对数据所有者和用户身份认证处理,完成密钥的产生和分发,根据差分隐私特征将用户所有者的数据划分为不同类型,获取用户访问特征向量。构建全同态加密机制,同时引入代理重加密机制,通过密钥转换完成数据加密处理,最终实现云数据全同态加密优化。实验对比结果表明,上述加密方案能够快速完成数据加密处理,有效确保数据的安全性。

基于同态加密的区块链混币方案

区块链混币技术是一种保护交易隐私、实现交易的不可链接性的重要方案。然而,其结合了Pedersen承诺的验证过程,需要数百字节的空间开销,极大地降低了可用性。利用国密SM2算法、同态加密和混淆地址,提出了一种新的区块链混币方案。该方案通过使用EC-ElGamal半同态加密技术加密交易金额,在链上交易过程中完全隐藏交易金额,将隐藏金额后的交易进行两次验证和一次重随机化后发送到一次性的混淆地址中,打破交易发起方和接收方的联系,实现了交易的不可链接性和不可追踪性。所提方案能够有效提高交易数据隐私保护的强度,增强对分析攻击、密钥重放攻击和女巫攻击的抵抗能力,同时单次交易的空间占用减少了82.25%,交易吞吐量显著提高。

基于综合评分的移动群智感知隐私激励机制

移动群智感知系统(MCS)能否高效地运行,很大程度上取决于是否有大量任务参与者参与到感知任务中。然而在现实中,用户的感知成本增加以及用户的隐私泄露等原因,导致用户的参与积极性不高,因此需要一种有效的手段,用于在保证用户隐私安全的同时,还能促进用户积极地参与到任务中。针对上述问题,结合本地化差分隐私保护技术,提出了一种基于综合评分的双边拍卖隐私激励机制(Privacy Incentive Mechanism of Bilateral Auction with Comprehensive Scoring, BCS),这种激励机制包括拍卖机制、数据扰动和聚合机制以及奖励和惩罚机制3个部分。拍卖机制综合考虑了各种因素对用户完成感知任务的影响,在一定程度上提高了任务的匹配程度;数据扰动和聚合机制在隐私保护和数据精度之间做出权衡,在保证数据质量的同时做到了对用户隐私的良好保护;奖励和惩罚机制奖励诚信度和活跃度高的用户,激励用户积极参与感知任务。实验结果表明,BCS可以在提高平台收益和任务匹配率的同时保证感知数据的质量。

支持受控共享的医疗数据隐私保护方案

患者医疗健康数据信息的合理利用促进了医学研究机构的发展。针对目前患者与医疗研究机构间共享医疗数据隐私易泄露,以及患者对医疗数据的使用情况不可控的问题,提出一种支持受控共享的医疗数据隐私保护方案。首先,将区块链与代理服务器结合设计医疗数据受控共享模型,区块链矿工节点分布式构造代理重加密密钥,使用代理服务器存储和转换医疗数据密文,利用代理重加密技术在保护患者隐私的同时实现医疗数据安全共享。其次,设计用户权限动态调整机制,由患者与区块链授权管理节点交互通过授权列表来更新医疗数据访问权限,实现患者对医疗数据的可控共享。最后,安全性分析表明,所提方案可以在医疗数据隐私保护的同时,实现医疗数据动态共享,并且可以抵抗共谋攻击。性能分析表明,该方案在通信开销、计算开销方面具有优势,适用于患者或医院与研究机构间的数据受控共享。

基于区块链隐私保护的工业网络输出指令安全控制算法设计

当前工业网络输出指令加密机制多为目标式和独立式,导致指令安全控制效果不理想,丢包率增加,为此提出基于区块链隐私保护的工业网络输出指令安全控制算法。设计工业网络指令安全控制辨识目标,将此目标导入初始的安全指令控制加密程序中,设置一个基础的控制指令。以其作为引导,进行安全控制目标的扩展及生成,对指令的内容进行多层级加密。在指令加密处理的基础上,根据输出指令安全控制指标及参数的限制,设计不同的安全控制标准和双向控制条件,将安全控制辨识目标导入当前的区块链双向控制结构中,形成一个具体细化的控制结构。在控制结构中,利用区块链技术将区块控制程序与重叠指令的控制程序关联起来,实现动态化的指令输出安全控制。实验结果表明:这种安全控制算法能够对全部工业网络输出指令数据进行加密处理,能够最大程度上保证工业网络输出指令隐私安全,丢包率在5.2%以下,控制正确率较高,具有较高的实际应用价值。

拍卖机制驱动的数据激励共享方案

云计算、大数据、物联网等技术的发展为数据共享奠定了基础,当前各国政府都在鼓励数据共享和交易,以促进数据之间的互联互通,刺激大数据经济的发展。由于数据本身含有许多隐私信息以及数据共享过程缺乏激励,大多数据拥有者不愿参与共享,基于此出现了将拍卖与隐私保护结合起来的数据共享方案。但在这些拍卖方法中,没有考虑到数据底价问题,并且忽略了数据请求者的相关利益。为此,基于拍卖机制设计了一种满足双方利益的数据激励共享方案。采用基于属性的访问控制技术防止未授权的请求者进入拍卖环节;结合现实场景中的拍卖使用信息熵为数据设置硬性底价,为了防止拍卖过程中请求者过度夸大拥有者的数据价值造成获胜者后悔的情况,设置软性底价作为拥有者理想的最高成交价;应用Pedersen承诺对请求者出价进行隐藏,并使用零知识范围证明对各请求者的出价信息进行验证,利用区块链实现数据交易结果的公开问责。实验分析表明,所设计的拍卖方案满足双方利益并且可以实现所有参与者的个体理性、弱预算平衡和激励相容等特性。

基于区块链的移动群智感知数据处理研究综述

针对移动群智感知(MCS)数据处理的用户广泛参与性、采集设备灵活移动性与通信环境复杂性的特点,对基于区块链的移动群智感知数据处理进行评估.回顾移动群智感知与区块链的发展历程,总结移动群智感知数据处理面临的挑战与区块链技术的特点;设计基于区块链的移动群智感知体系结构(BMCA),实现数据去中心化管理、数据安全保障、数据质量精准评估与激励可信性增强;从隐私保护、数据质量评估、激励机制3个维度,对比分析现有的数据处理关键技术研究工作;探讨基于区块链的移动群智感知数据处理研究在资源消耗控制、数据精准分析、全周期与差异化隐私保护、融合模式应用等方面存在的问题及未来可能的发展方向.

基于区块链的物联网多域数据共享机制

针对物联网中传感设备之间的传感信息数据共享面临的安全性和隐私保护问题,文中提出了一种基于区块链的多域数据共享机制。该机制将数据共享问题视为数据交易问题,引入可信的监管机构来保证交易买卖双方的利益。该机制提供了一种数据发布方法,买家可以将数据安全地发布到云存储服务中,并在发布过程中不会泄露数据信息。在数据交易过程中,为保护买家的隐私不被泄露,采用环签名对买家许可证进行签名和验证。此外,该机制提供了一种匿名评价方法,买家可在不透露身份的情况下对交易进行评价并监督卖方行为。安全性分析和仿真实验结果表明,该机制安全性良好,具有高效性并符合物联网数据安全共享的要求。

基于混合链的协作业务流程的隐私保护和数据监管

协作业务流程可以在执行跨组织的业务目标时简化业务逻辑,提升协作效率。它有两个重要的管理目标:隐私保护和数据监管。协作者们通常会利用可信的第三方平台。然而这种中心化的协作会存在监管失效和隐私泄露的风险。因此,目前的研究转向了利用去中心化方式的区块链技术实现流程监管。目前的方法基于匿名身份和公开的数据,会造成数据泄露的风险。为此,提出一个基于混合链的协作业务流程管理新方法,在监管业务流程数据的基础上,设计了对流程数据的针对性隐私保护方案。公有链部分确保流程实例对外部用户的隐私性和可监管性,联盟链部分提供了对内部协作者的隐私保护和数据监管。当流程实例在联盟链上执行时,利用三次遍历全体协作者的双向传递机制隔离协作任务。通过在多个公开的业务流程数据上的仿真实验,对比了混合链方法与其他基于区块链的方法。实验结果表明,混合链方法能够保护隐私数据和监管流程数据,在执行效率上具备可用性。

群智感知中基于区块链的共识节点招募方法

随着移动终端的快速普及,群智感知系统得到了学术界的广泛关注.现有工作大多数依赖第三方平台来执行参与者选择和收集大规模数据,很难兼顾对参与者隐私的保护和数据质量评估的公平.针对该问题,本文提出了一种群智感知中基于区块链的共识节点招募方法.通过Pedersen承诺隐藏参与者的选举随机值,借助零知识证明系统和区块链技术保证了选举数据的隐私性和完整性,实现在参与者中公平且秘密地选举单个共识节点.考虑到单点故障的隐患问题,该方法支持定时重启选举,保持共识节点不断更新以抵御共谋攻击;还结合Pedersen承诺设计了更新阶段,安全定期更新选举随机值以抵御动态腐化.结合区块链对数据质量评估结果进行二次共识后,由共识节点基于贡献更新信誉和计算报酬,保证了参与者的奖惩公平并激励参与者提供高质量的任务数据,从而提高用户参与任务的积极性.仿真实验表明,该方法不仅实现了安全公平的选举过程,而且不会额外增加参与者的计算和存储负担.

基于区块链的跨数据中心匿名可监管身份认证方案

随着大数据和云计算技术的发展,金融机构之间跨机构数据共享交互日益增多。由于隐私泄露事件的频繁发生,数据安全保护面临着严峻的挑战,也对跨数据中心的身份认证等数据安全管理技术提出了更高的要求。传统的中心化身份认证方案在安全性和可扩展性方面存在缺陷,难以满足高频跨中心数据共享的应用需求。区块链具有分布式、难以篡改、可追溯、多方共识等优异特性,为跨数据中心身份认证提供了崭新的思路。在实际应用场景中,不仅要对用户的身份隐私进行保护,还需要对用户的匿名身份进行有效的监管。提出了一种基于区块链的匿名可监管的跨数据中心身份认证方案,不改变各数据中心原有架构,以各数据中心的管理节点为中心建立分布式多中心的联盟链;利用区块链技术,保障身份认证过程的透明性和可信性;采用属性签名保护用户身份隐私,并建立身份映射表追踪用户真实身份,实现用户身份隐私保护和监管兼顾的跨数据中心身份认证。安全性分析和仿真实验结果表明,该方案无须用户再次注册,具有不可伪造性和可追踪等特性,能够抵御重放攻击、拒绝服务攻击、篡改攻击、中间人攻击等网络攻击。

物联网环境下分布式的隐私保护数据聚合方案

为了解决在物联网场景下数据聚合中存在的中心化存储、隐私信息泄露、依赖可信第三方等问题,本文提出了工业物联网环境下分布式的隐私保护数据聚合方案(Distribued Privacy-Preserving Data Aggregation scheme,DPPDA).首先通过区块链技术与雾计算设计了一个分层分布式存储数据聚合架构,实现数据的去中心化存储;并且提出了基于阈值同态加密算法结合随机值噪声值对数据加密,实现用户数据隐私保护并且系统不依赖于完全可信的密钥管理中心;此外,结合Bloom过滤器与批量验证签名算法设计了一个高效的匿名签名验证机制,实现用户身份的隐私保护.安全分析和仿真测试验证本方案安全可行,有较好的抗攻击性和较低的计算成本.结果表明,本方案能够在物联网的数据聚合过程中保护用户数据隐私并具有较好性能.