基于隐私同态数据融合的完整性验证协议

在无线传感器网络中的安全数据融合能够有效防止隐私泄露和数据篡改等问题,并实现高效的数据传输。由此提出一种基于隐私同态数据融合的完整性验证协议IV-PHDA。该协议采用同态加密保证数据隐私性;利用随机检测节点对节点聚合结果的完整性进行检测,以验证聚合节点是否忠实地传输每个数据分组。通过理论分析和仿真对比,对其算法的性能进行验证,结果表明,该协议能够在网络传输的过程中检测数据的完整性,并且实现较好的隐私保护和较高的数据精确度。     

一种支持完整性验证的隐私保护直方图融合算法

针对无线传感器网络隐私保护数据融合和完整性验证难以同时兼顾问题,提出一种支持完整性验证的隐私保护直方图融合算法（iPPHA ）。构建两棵融合树,分别传输融合数据和冗余信息,在基站处对融合结果的完整性进行验证。针对数据包丢失问题,设计了一种ID传输方案来提高可靠性。仿真结果显示,算法可以在不明显增加网络资源消耗的前提下,进行完整性验证。改进型ID传输方案可节约70％的通信开销。     

一种可检测数据完整性的隐私数据融合算法

针对无线传感器网络数据融合中可能出现的数据篡改和隐私泄露等问题,该文提出一种可进行完整性检测的无线传感器网络隐私数据融合算法 ICKPDA.该算法首先在感知数据中嵌入私密种子,对真实数据进行隐藏;然后通过数据分片和聚集技术,增强数据的隐私保护性;最后利用数据间的关联特性在基站进行完整性检测.仿真结果显示,相比于其它算法,ICKPDA 在保证融合结果精确的前提下,能有效地进行数据完整性检测和隐私保护,同时花费较少的数据通信量和计算量.     

具有隐私保护的云存储访问控制方案

针对传统的访问控制方案无法在云计算环境下保护用户的属性隐私,提出了具有隐私保护的云存储访问控制方案。采用混合加密体制实现了数据的机密性,即利用对称密钥加密明文数据,再利用公钥密码体制对对称密钥进行加密。在新的访问控制方案中,公钥加密采用了匿名的密文策略下基于属性的加密技术。安全性分析表明,新方案在保护用户属性隐私的同时,达到了选择明文安全性,可抵抗恶意用户及云存储服务器的合谋攻击。     

NewSQL数据库技术在用户隐私数据安全保护中的应用

为增强用户隐私数据的加密效果,提升整体的安全程度,营造更加稳定的应用环境。对NewSQL数据库技术在用户隐私数据安全保护中的应用分析。进行数据预处理,提取隐私数据特征,建立等距加密保护结构,以此为基础,设计NewSQL协同加密模型,将分布式动态NewSQL数据库引入数据防护程序内部,采用HTAP混合处理完成用户隐私数防护。测试结果表明,文章所设计的NewSQL数据库加密测试组最终得出的安全系数相对较高,表明在实际应用的过程中,对于用户的隐私数据以及身份信息保密程度更高一些,具有实际的应用价值。     

基于隐私云的自毁邮件保护系统设计与实现

随着云计算的发展,云数据安全越来越受到企业等云服务用户的关注。过时、归档的云端数据有泄漏风险,传统加密方案不能全面地满足隐私保护的需求。近年来电子邮件泄密事件频发,电子邮件的数据安全,尤其是企业邮件安全是云数据安全中最为突出的问题之一。文中提出的基于隐私云的数据自毁方法能够实现云数据过期后自动销毁,并基于该方法实现了自毁邮件保护系统,通过测试原型系统,可以实现邮件到期自毁,保护用户隐私不被泄漏。     

基于内积谓词的属性基隐私保护加密方案

隐私保护是信息安全中的热点话题,其中属性基加密(ABE)中的隐私问题可分为数据内容隐私、策略隐私及属性隐私。针对数据内容、策略和属性3方面隐私保护需求,该文提出基于内积谓词的属性基隐私保护加密方案(PPES)。所提方案利用加密算法的机密性保障数据内容隐私,并通过向量承诺协议构造策略属性及用户属性盲化方法,实现策略隐私及属性隐私。基于混合论证技术,该文证明了所提方案满足标准模型下适应性选择明文安全,且具备承诺不可伪造性。性能分析结果显示,与现有方法相比,所提方案具有更优的运行效率。     

基于同态MAC的无线传感网安全数据融合方案

数据融合是缓解无线传感网资源瓶颈的重要方法之一,但在开放环境中易受数据机密性和完整性攻击。针对此问题,该文提出一种基于同态MAC的无线传感网安全数据融合方案SDA-HMAC。通过同态MAC技术进行融合数据的完整性检测,利用同态加密算法保证了融合数据的机密性,使用杂凑函数和时间参数t计算密钥的MAC保证了数据的新鲜性。实验仿真和理论分析表明,相比于其它方案,SDA-HMAC方案在传感网数据融合过程中能提供较好的数据机密性、完整性和新鲜性保护,具有较高的数据传输效率和融合精度,同时花费较少的计算量和通信量。     

基于同态加密的DBSCAN聚类隐私保护方案

为了降低数据外包聚类运算过程中存在的隐私泄露风险,提出了一个基于同态加密的DBSCAN聚类隐私保护方案。为了加密实际场景中的浮点型数据,给出了针对不同数据精度的3种数据预处理方式,并提出了一种基于数据特点且综合考虑数据精度和计算开销等方面的数据预处理方式的选择策略。由于同态加密不支持密文比较运算,设计了一个用户端与云服务器之间的协议实现密文比较功能。理论分析和实验结果表明,所提方案能够保证数据隐私安全,并且具有较高的聚类准确率和较低的时间开销。     

一种增强的个性化匿名隐私保护模型改进

隐私保护是信息安全的重要研究方向,为了提高隐私保护能力,提出一种基于椭圆曲线同态加密的增强性个性化匿名隐私保护模型。采用K-匿名编码方法进行隐私保护信息的编码设计,构建加密密钥,结合分段线性混沌映射方法进行隐私保护模型的算术编码设计,采用椭圆曲线同态加密算法进行个性化匿名隐私保护增强设计,提高信息加密的深度,实现隐私保护优化。仿真结果表明,采用该方法进行隐私保护信息加密和隐私保护抗攻击能力较强,信息泄露的风险大大降低。     

一种改进的动态数据聚集算法及仿真

针对当前计算数据中心中的动态数据分配方法中存在局限性强、公式复杂、算法运行效率低等问题,提出了动态数据聚集算法,结合计算数据中心实际情况进行改进,分析了动态数据聚集算法在计算数据中心的应用。仿真结果表明,与常规数据分配方法相比,改进后的基于动态数据聚集算法的数据分配方法,数据分配准确率可达到98.2%,相比常规方法有效地提升了结果的准确率。     

无线传感器网络中有效的安全数据融合机制

在无线传感器网络中,数据融合是实现有效传输和节省能源的一个重要途径,许多应用都需要可靠并且可信的数据来进行融合.针对上述要求,提出了一个新的安全数据融合算法来保证融合数据的机密性和完整性.算法使用端到端加密和逐跳加密相结合的方式进行数据传输,通过认证过程进行恶意节点及伪造数据的检测.仿真表明,提出的算法能够有效地检测出恶意节点,并保证融合结果的准确性.     

一种结合多重插补的ADS-B数据筛选算法

为了提高广播式自动相关监视(Automatic Dependent Surveillance-Broadcast,ADS-B)报文质量,解决由于地面站多重覆盖、硬件设施配置、运行状态等原因引起的报文在数据重复性、数据完整性和实时性上存在的问题,采用多条件直接起始法建立航迹,采用多项约束条件筛选进入优选的报文,并基于专家评级法对报文中表征数据项完整性的权重参数进行估计,结合报文的位置精度和完好性参数综合计算得出ADS-B数据质量指标并将其作为挑选依据。对于ADS-B报文质量极度不理想的特殊情况,使用多重插补(Multiple Imputation,MI)算法对其进行补全处理,综合以上步骤挑选出高质量和高可靠性的ADS-B更新点迹。仿真计算证明该方法可有效剔除重复覆盖造成的重复报文和网络延迟造成的历史数据,防止航迹点回跳,提高数据质量和监视效率。目前,该方法已实际应用于地面站和传输链路条件均不理想的场合,大量实践结果表明该方法可用于保证输出高实时性的、周期性的、稳定平滑的、可靠的ADS-B数据。     

Data relationship degree–based clustering data aggregation for VANET

Data aggregation is one of the major needs of vehicular ad hoc networks (VANETs) due to the constraints of resources. Data aggregation in VANET can reduce the data redundancy in the process of data gathering and thus conserving the bandwidth. In realistic applications, it is always important to construct an effective route strategy that optimises not only communication cost but also the aggregation cost. Data aggregation at the cluster head by individual vehicle causes flooding of the data, which results in maximum latency and bandwidth consumption. Another approach of data aggregation in VANET is sending local representative data based on spatial correlation of sampled data. In this article, we emphasise on the problem that recent spatial correlation data models of vehicles in VANET are not appropriate for measuring the correlation in a complex and composite environment. Moreover, the data represented by these models is generally inaccurate when compared to the real data. To minimise this problem, we propose a group-based data aggregation method that uses data relationship degree (DRD). In the proposed approach, DRD is a spatial relationship measurement parameter that measures the correlation between a vehicle’s data and its neighbouring vehicles’ data. The DRD clustering method where grouping of vehicle’s data is done based on the available data and its correlation is presented in detail. Results prove that the representative data using proposed approach have a low distortion and provides an improvement in packet delivery ratio and throughput (up to of 10.84% and 24.82% respectively) as compared to the other state-of-the-art solutions like Cluster-Based Accurate Syntactic Compression of Aggregated Data in VANETs.     

EEPPDA—Edge-enabled efficient privacy-preserving data aggregation in smart healthcare Internet of Things network

The Internet of Things-based smart healthcare provides numerous facilities to patients and medical professionals. Medical professionals can monitor the patient's real-time medical data and diagnose diseases through the medical health history stored in the cloud database. Any kind of attack on the cloud database will result in misdiagnosis of the patients by medical professionals. Therefore, it becomes a primary concern to secure private data. On the other hand, the conventional data aggregation method for smart healthcare acquires immense communication and computational cost. Edge-enabled smart healthcare can overcome these limitations. The paper proposes an edge-enabled efficient privacy-preserving data aggregation (EEPPDA) scheme to secure health data. In the EEPPDA scheme, captured medical data have been encrypted by the Paillier homomorphic cryptosystem. Homomorphic encryption is engaged in the assurance of secure communication. For data transmission from patients to the cloud server (CS), data aggregation is performed on the edge server (ES). Then aggregated ciphertext data are transmitted to the CS. The CS validates the data integrity and analyzes and processes the authenticated aggregated data. The authorized medical professional executes the decryption, then the aggregated ciphertext data are decrypted in plaintext. EEPPDA utilizes the batch verification process to reduce communication costs. Our proposed scheme maintains the privacy of the patient's identity and medical data, resists any internal and external attacks, and verifies the health data integrity in the CS. The proposed scheme has significantly minimized computational complexity and communication overhead concerning the existing approach through extensive simulation.     

基于有限射影几何的细粒度数据完整性检验方法

 细粒度的数据完整性检验方法在实现完整性检验的同时可以对少数的错误对象进行准确和高效的隔离,从而避免因偶然错误或个别篡改造成整体数据失效的灾难性后果.对细粒度数据完整性检验问题进行了总结和分类,给出了总体的研究思路.为了提高细粒度数据完整性检验方法的错误指示效率,基于有限射影几何原理构造了一种新的数据完整性指示码.其思想是将有限射影几何空间中点与线的关联关系映射为Hash与数据对象之间的监督关系,实现Hash之间完全的均匀交叉.分析了码的主要性能.分析和实验结果表明该码可以准确指示多个错误并且具有更高的压缩率.     

Layered data aggregation with efficient privacy preservation for fog‐assisted IIoT

The emergence of fog computing facilitates industrial Internet of Things (IIoT) to be more real‐time and efficient; in order to achieve secure and efficient data collection and applications in fog‐assisted IIoT, it usually sacrifices great computation and bandwidth resources. From the low computation and communication overheads perspective, this paper proposes a layered data aggregation scheme with efficient privacy preservation (LDA‐EPP) for fog‐assisted IIoT by integrating the Chinese remainder theorem (CRT), modified Paillier encryption, and hash chain technology. In LDA‐EPP scheme, the entire network is divided into several subareas; the fog node and cloud are responsible for local and global aggregations, respectively. Specially, the cloud is able to obtain not only the global aggregation result but also the fine‐grained aggregation results of subareas, which enables that can provide fine‐grained data services. Meanwhile, the LDA‐EPP realizes data confidentiality by the modified Paillier encryption, ensures that both outside attackers and internal semi‐trusted nodes (such as, fog node and cloud) are unable to know the privacy data of individual device, and guarantees data integrity by utilizing simply hash chain to resist tempering and polluting attacks. Moreover, the fault tolerance is also supported in our scheme; ie, even though some IIoT devices or channel links are failure, the cloud still can decrypt incomplete aggregation ciphertexts and derive expected aggregation results. Finally, the performance evaluation indicates that our proposed LDA‐EPP has less computation and communication costs.     

A secure electric energy management in smart home

We formulate and study an intelligent and secure house electricity system on the basis of the Internet of Things. The security of sensitive data collected and transmitted by sensor nodes installed in home appliances and household electrical devices is critical, since the transmitted data can be easily manipulated by different types of attacks. The confidentiality and integrity of household electrical devices information must be assured to insure appropriate and timely response. Providing a secure aggregation mechanism is thus very essential to protect the integrity and the privacy of data aggregation. In this paper, we propose a secure data aggregation scheme that exploits compressed sensing (CS) to reduce the communication overhead of collected electrical power measurement. Then, the data will be encrypted by each sensor node after the compressing phase, and a cryptography hash algorithm is used to ensure data integrity. Finally, we apply an aggregation function for data priorities and then send the data for diagnosis. Then, we will present simulation results for the evaluation of the proposed electric energy management system.