面向物联网的隐私数据安全问题研究

随着社会的发展,物联网已成为社会发展的重要新兴产业,在各个领域中广泛应用。物联网是基于互联网技术产生的,在物联网的运行过程中势必会产生大量数据,这些数据都是客户的隐私,切实保护好客户隐私是物联网进一步发展的首要条件。在面向物联网的隐私数据安全问题时,相关技术人员一定要清楚威胁物联网隐私数据安全的主要途径,加大安全防护力度,保护人们的隐私。文章从信息获取、信息传输以及信息处理3个途径,对隐私数据安全问题进行探讨,并提出一些加大隐私安全防护的举措。     

基于联邦学习的分布式数据安全融合方法研究

为满足在可用性、实用性的前提下实现大数据安全融合共享,设计了基于联邦学习的分布式数据安全融合模型,并运用差分隐私、安全多方计算、同态加密、函数加密四种方法进行实现,以保证分布式数据融合的安全性和可行性.     

基于云计算的大数据安全隐私保护研究

随着社会的发展,人们生活水平的提高,资源共享已经成为了社会分工的必然趋势.在这种大背景下,云计算被应用到了各行各业当中.云计算具备快速部署和按需服务等特点,一经应用就赢得了广大民众的高度好评.本文以安全隐私保护基本需求为切入点,系统地阐述了基于云计算的大数据安全与隐私保护的相关理论,旨在进一步拓展云计算的应用范围.     

基于云计算的大数据安全隐私保护的研究

随着社会进步和发展,资源共享和社会分工是必然趋势,公共云平台逐渐成为互联网、电网重要国家基础设备,已经开始广泛应用云计算。其中最重要就是云计算数据安全,集中化数据基础上重视云计算隐私安全问题,云计算拥有快速部署、按需服务、动态弹性、节约经费等特点得到广泛应用。     

基于几何变形的大数据安全隐私保护方法

隐私保护已经成为大数据安全的重要研究内容之一。在分析了影响大数据安全隐私三个方面的基础上,提出了一种基于几何变形的大数据安全隐私保护方法。该方法从数据源的角度出发,使用几何变形的方法对数据进行干扰,使得数据聚类算法失效或分析得出错误的结果,从而达到大数据安全隐私保护的目的。在实际使用中,该方法效果良好。     

关于物联网信息安全与隐私保护的研究

互联网的不断发展,极大地推动了物联网的诞生,这被誉为世界信息产业的第三次浪潮。它对人们的日常生活以及生产模式都产生了较大的影响,并且也造成了一定的安全隐患,这是物联网行业目前需要尽快解决的关键问题。     

物联网通信中数据隐私保护方法

针对当前数据隐私保护方法在物联网通信过程中存在通信路径破解率较高、隐私数据暴露概率较高且数据丢包率不易控制的问题,提出物联网通信中数据隐私保护方法。采集汇总各节点数据,使用特征数据融合技术,完成通信数据预处理,设定物联网专用加密函数,应用数据同态加密算法,对数据进行点对点加密传输,确定数据传输节点,设定数据隐私传输过程,选择合适传输路径,实现数据隐私保护。构建实验环节,实验结果表明：此方法可有效降低通信路径破解率以及隐私数据暴露概率,进一步控制数据传输过程中的丢包率,保证数据完整度,提升数据传输安全。     

基于最小生成树路由的电力线载波通信数据融合算法

电力线载波通信技术利用现有的电力传输网络进行数据传输,具有建设成本低,部署快速灵活等特点,被广泛用于局域网本地通信中。但当节点数过多或传输数据量过高时,如每个节点单独将数据传到调控中心,将会给整个通信系统带来巨大的数据量,同时降低通信效率,甚至造成通信拥塞。为降低传输数据量,节省通信传输资源,提升电力线载波接入网络的传输能力,本文提出了基于最小生成树传输路径的电力线载波通信数据融合算法。将所提算法与未使用融合算法带来的数据量进行对比,证明了所提算法的有效性。     

传感器网络数据融合隐私保护及安全框架

在传感器网络中,隐私保护和入侵检测是一对矛盾关系,调和两者的矛盾非常重要。在传感器网络中传感数据融合是一个基本操作,研究隐私保护和入侵检测在传感数据融合中的关系并提出一个框架,可以探测错误数据融合,但不需要知道实际的传感数据内容,因而得以保证传感数据的隐蔽性。实验结果显示,实际的原始数据和聚合传感数据可以得到很好隐蔽的同时能够检测到大部分错误数据融合。     

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.     

High energy‐efficient and privacy‐preserving secure data aggregation for wireless sensor networks

An efficient data process technology is needed for wireless sensor networks composed of many sensors with constrained communication, computational, and memory resources. Data aggregation is presented as an efficient and significant method to reduce transmitted data and prolong lifetime for wireless sensor networks. Meanwhile, many applications require preserving privacy for secure data aggregation. In this paper, we propose a high energy‐efficient and privacy‐preserving scheme for secure data aggregation. Because of the importance of communication overhead and accuracy, our scheme achieves less communication overhead and higher data accuracy besides providing for privacy preservation. For extensive simulations, we evaluate and conclude the performance of our high energy‐efficient and privacy‐preserving scheme. The conclusion shows that the high energy‐efficient and privacy‐preserving scheme provides better privacy preservation and is more efficient than existing schemes. Copyright © 2012 John Wiley & Sons, Ltd.     

An efficient data aggregation scheme with local differential privacy in smart grid

By integrating the traditional power grid with information and communication technology, smart grid achieves dependable, efficient, and flexible grid data processing. The smart meters deployed on the user side of the smart grid collect the users' power usage data on a regular basis and upload it to the control center to complete the smart grid data acquisition. The control center can evaluate the supply and demand of the power grid through aggregated data from users and then dynamically adjust the power supply and price, etc. However, since the grid data collected from users may disclose the user's electricity usage habits and daily activities, privacy concern has become a critical issue in smart grid data aggregation. Most of the existing privacy-preserving data collection schemes for smart grid adopt homomorphic encryption or randomization techniques which are either impractical because of the high computation overhead or unrealistic for requiring a trusted third party.In this paper, we propose a privacy-preserving smart grid data aggregation scheme satisfying Local Differential Privacy (LDP) based on randomized responses. Our scheme can achieve an efficient and practical estimation of power supply and demand statistics while preserving any individual participant's privacy. Utility analysis shows that our scheme can estimate the supply and demand of the smart grid. Our approach is also efficient in terms of computing and communication overhead, according to the results of the performance investigation.     

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.     

安全的WSN数据融合隐私保护方案设计

针对无线传感器网络数据融合过程中的数据隐私和完整性保护问题,提出一种安全的数据融合隐私保护方案(SPPDA),把节点的私密因子与原始数据构成复数,采用同态加密方法对复数进行加密,实现在密文不解密的情况下进行数据融合,同时采用基于复数的完整性验证方法,确保数据的可靠性。理论分析和仿真结果表明,SPPDA方案的计算代价和通信开销较少,数据融合的精确度高。     

云计算中数据隐私保护研究进展

由于社会分工和资源共享的必然,公共云平台必将成为和电网、互联网等同等重要的国家基础设施。云计算面临的安全问题制约着云计算的广泛使用。数据安全在云计算中尤为重要,如何保证数据的安全性是云计算安全的核心。从数据的隐私保护计算、数据处理结果的完整性认证、数据访问权限控制以及数据的物理安全4个方面对已有研究工作进行了分类和总结,为后续云计算中数据的安全性研究提供参照。     

State-of-the-art survey of privacy-preserving data aggregation in wireless sensor networks

A state-of-the-art survey of privacy-preserving data aggregation techniques in wireless sensor networks was reviewed.Firstly,preliminaries were introduced,including network models,adversary models,and performance evaluation metrics.Secondly,existing related work was classified into several types according to privacy preservation techniques,such as homomorphic encryption,data perturbation,slicing-mixing technique,generalization,secure multiparty computation,and the key mechanisms of typical protocols were elaborated and analyzed.Finally,the promising future research directions were discussed.     

MDPA: multidimensional privacy‐preserving aggregation scheme for wireless sensor networks

In this paper, we propose a novel multidimensional privacy‐preserving data aggregation scheme for improving security and saving energy consumption in wireless sensor networks (WSNs). The proposed scheme integrates the super‐increasing sequence and perturbation techniques into compressed data aggregation, and has the ability to combine more than one aggregated data into one. Compared with the traditional data aggregation schemes, the proposed scheme not only enhances the privacy preservation in data aggregation, but also is more efficient in terms of energy costs due to its unique multidimensional aggregation. Extensive analyses and experiments are given to demonstrate its energy efficiency and practicability. Copyright © 2009 John Wiley & Sons, Ltd.     

A secure channel code‐based scheme for privacy preserving data aggregation in wireless sensor networks

Data aggregation is an efficient method to reduce the energy consumption in wireless sensor networks (WSNs). However, data aggregation schemes pose challenges in ensuring data privacy in WSN because traditional encryption schemes cannot support data aggregation. Homomorphic encryption schemes are promising techniques to provide end to end data privacy in WSN. Data reliability is another main issue in WSN due to the errors introduced by communication channels. In this paper, a symmetric additive homomorphic encryption scheme based on Rao‐Nam scheme is proposed to provide data confidentiality during aggregation in WSN. This scheme also possess the capability to correct errors present in the aggregated data. The required security levels can be achieved in the proposed scheme through channel decoding problem by embedding security in encoding matrix and error vector. The error vectors are carefully designed so that the randomness properties are preserved while homomorphically combining the data from different sensor nodes. Extensive cryptanalysis shows that the proposed scheme is secure against all attacks reported against private‐key encryption schemes based on error correcting codes. The performance of the encryption scheme is compared with the related schemes, and the results show that the proposed encryption scheme outperforms the existing schemes.     

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。