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A Lightweight and Robust multidimensional data aggregation Scheme for IoT

IEEE INTERNET OF THINGS JOURNAL 2024年第2期11卷 3578-3588页

作者： Zhang, Ming Li, Yanping Ding, Yong Yang, Bo Shaanxi Normal Univ Sch Math & Stat Xian 710119 Peoples R China Guilin Univ Elect Technol Sch Comp Sci & Informat Secur Guilin Peoples R China Shaanxi Normal Univ Sch Comp Sci Xian 710119 Peoples R China

data aggregation technology plays a very important role in improving the efficiency of data collection of the Internet of Things (IoT). Most data collected by sensor nodes (SNs) in IoT is multidimensional. However, there are a few existing multidimensional data aggregation schemes, which are almost based on homomorphic encryption and not suitable for resource-constrained IoT smart devices. In addition, when SNs cannot upload in time for some reason, such as device error or network interruption, the control center cannot get the correct aggregation result, i.e., robustness is not considered in most schemes. Therefore, we propose a lightweight and robust multidimensional data aggregation scheme for IoT. First, multidimensional data is packaged into 1-D data based on the Chinese remainder theorem (CRT), which greatly reduces communication and storage overhead. Second, the encryption in our scheme only uses addition operations without the costly additive homomorphic encryption. At the same time, our proposed scheme supports batch verification, which reduces the computation complexity of bilinear pairs by nearly half. Third, our scheme also supports dynamic SNs management and fault tolerance, enabling scalability and robustness. Finally, performance evaluation shows that our scheme has lower communication overhead and is more suitable for resource-constrained IoT scenarios.

关键词： Chinese remainder theorem (CRT) Internet of Things (IoT) lightweight multidimensional data aggregation privacy protection

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EPMDA-FED: Efficient and Privacy-Preserving multidimensional data aggregation Scheme With Fast Error Detection in Smart Grid

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IEEE INTERNET OF THINGS JOURNAL 2022年第9期9卷 6922-6933页

作者： Liu, Zhusen Cao, Zhenfu Dong, Xiaolei Zhao, Xiaopeng Liu, Tian Bao, Haiyong Shen, Jiachen East China Normal Univ Shanghai Key Lab Trustworthy Comp Shanghai 200062 Peoples R China Donghua Univ Sch Comp Sci & Technol Shanghai Peoples R China East China Normal Univ MoE Engn Res Ctr Software Hardware Codesign Techn Shanghai 200062 Peoples R China East China Normal Univ Software Engn Inst Shanghai 200062 Peoples R China Zhejiang Gongshang Univ Sch Comp Sci & Informat Engn Hangzhou 310018 Peoples R China

Smart grids bring advantages of reliability and high efficiency by real-time communication technologies in contrast to the traditional grids. However, smart grids also raise concerns about privacy and security for the individual fine-grained information collection. In order to guarantee privacy and security in the grids, we propose an efficient and privacy-preserving multidimensional data aggregation scheme without a third trusted party and supporting fast error detection, named EPMDA-FED, in the article. First, we adopt a Chinese Remainder Theorem (CRT) to pack multidimensional data and encrypt the processed data using the keys generated by the negotiation among users and the control center (CC). Second, our scheme is efficient for encryption without high-cost additive homomorphic public-key encryption (PKE) scheme, such as the Paillier cryptosystem and supporting batch verification with fast error detection. Our proposed error detection algorithm is quite efficient with sublinear computational complexity. Besides, through security analysis, EPMDA-FED is semantically secure against collusion attack and the consistency of negotiated key, authenticity, and data integrity of the users' reports are guaranteed. Finally, performance evaluation shows EPMDA-FED is more efficient than the existing competing approaches in terms of computational and communication overheads.

关键词： data aggregation Smart grids Security Smart meters Costs Real-time systems Detection algorithms Batch verification error detection multidimensional data aggregation privacy preserving smart grid

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VMEMDA: Verifiable multidimensional Encrypted Medical data aggregation Scheme for Cloud-Based Wireless Body Area Networks

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IEEE INTERNET OF THINGS JOURNAL 2024年第10期11卷 18647-18662页

作者： Zhao, Jie Huang, Hejiao Zhang, Xiaojun He, Daojing Choo, Kim-Kwang Raymond Jiang, Zoe Lin Harbin Inst Technol Shenzhen Sch Comp Sci & Technol Shenzhen 518055 Guangdong Peoples R China BGP Inc China Natl Petr Corp Natl Engn Res Ctr Oil & Gas Explorat Comp Software Zhuozhou 072751 Peoples R China Southwest Petr Univ Sch Comp Sci & Software Engn Chengdu 610500 Peoples R China Univ Texas San Antonio Dept Informat Syst & Cyber Secur San Antonio TX 78249 USA Pengcheng Lab Dept New Networks Shenzhen 518055 Guangdong Peoples R China

Compared to conventional wireless body area networks (WBANs), the amount of data processed and the analytical capabilities offered by cloud-based WBANs are significantly more extensive. Nevertheless, the paramount consideration in such contexts remains the security and privacy ramifications. Concurrently, the process where medical cloud server (MCS) computes the response aggregation data may be opaque and there is a risk that (partially) invalid aggregation results may be presented to the task requester, either intentionally (e.g., malicious or cost-saving) or unintentionally (e.g., corruption or processing error). Furthermore, with the different roles played by each data requester, relying solely on a single data aggregation type is no longer sufficient to satisfy the diverse data aggregation requests from these requesters. To this end, this article proposes a novel verifiable multidimensional encrypted medical data aggregation scheme (VMEMDA) for cloud-based WBANs, where we integrate an extended super-increasing sequence with a modified Paillier cryptosystem. Doing so allows us to ensure that each dimensional medical data collected by wireless sensor devices and corresponding square values can be encrypted into a single ciphertext with the chronological time series. This enables MCS to select various aggregation types, such as spatial/temporal data aggregation, to aggregate the multisource encrypted medical data into a single ciphertext. Then, the task requester can conduct diverse privacy-preserving statistical analyses, including sum, average, and variance. Moreover, we utilize a homomorphic hash function to guarantee the encrypted data integrity in a highly efficient way, and we design an unpredictable random sequence and integrate it into the provable data possession mechanism to achieve aggregated data correctness guarantee. Performance evaluation demonstrates that VMEMDA exhibits considerably lower computation and communication overhead compared to other exi

关键词： data aggregation Cloud computing Task analysis data privacy Internet of Things data integrity Wireless sensor networks Aggregated data correctness guarantee encrypted data integrity (EDI) multidimensional data aggregation statistical analysis wireless body area networks (WBANs)

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Fault-Tolerant and Collusion-Resistant Lattice-Based multidimensional Privacy-Preserving data aggregation in Edge-Based Smart Grid

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IEEE INTERNET OF THINGS JOURNAL 2024年第6期11卷 9487-9504页

作者： Fan, Kai Ren, Yuanshuai Bai, Yuhan Wei, Guanglu Zhang, Kuan Li, Hui Yang, Yintang Xidian Univ Sch Cyber Engn State Key Lab Integrated Serv Networks Xian 710071 Shaanxi Peoples R China Xidian Univ Guangzhou Inst Technol State Key Lab Integrated Serv Networks Guangzhou 510700 Guangdong Peoples R China Univ Nebraska Lincoln Dept Elect & Comp Engn Lincoln NE 68588 USA Xidian Univ Key Lab Minister Educ Wide Band Gap Semicond Mat & Xian 710071 Peoples R China

The smart grid, which is an important component of smart cities, is developing rapidly nowadays, while the confidentiality and integrity of consumption data of customers become significant security issues. Although existing privacy-preserving aggregation schemes reduce the communication overhead and protect the privacy of users' multidimensional power data, most of them are vulnerable and possess no quantum-resistant properties. In this article, we combine the Chinese remainder theorem (CRT) and the bit decomposition method to provide multibit homomorphic properties for the quantum-resistant algorithm number theory research unit (NTRU), and propose the partial-homomorphic NTRU (PH-NTRU). Then, based on the algorithm, we design the lattice-based multidimensional data privacy-preserving data aggregation scheme named FTCR-LMPPDA, in which the edge devices work as aggregator gateways. Specifically, smart meters participating in the aggregation process share zero-sum numbers to resist collusion attacks. Error retransmission mechanism and random number reconstruction algorithm are introduced to enhance the robustness of this scheme and provide our system with the ability to recover from faults. In addition, security analysis shows our scheme can resist quantum attacks, collusion attacks, and other internal and external attacks as well as keep the security features, such as confidentiality, privacy and integrity of users' data. Finally, performance evaluation demonstrates that our scheme is more efficient than existing schemes and is more suitable for devices with constrained resources.

关键词： data aggregation Smart grids Security Resists Resistance data privacy Smart meters Collusion resistance edge-computing-based smart grid (ECSG) lattice-based homomorphic algorithm multidimensional data aggregation privacy-preserving

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Robust and Lightweight data aggregation With Histogram Estimation in Edge-Cloud Systems

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2024年第3期11卷 2864-2875页

作者： Su, Yuan Li, Jiliang Li, Jiahui Su, Zhou Meng, Weizhi Yin, Hao Lu, Rongxing Xi An Jiao Tong Univ Sch Cyber Sci & Engn Xian 710049 Peoples R China Xidian Univ Sch Math & Stat Xian 710071 Peoples R China Tech Univ Denmark Dept Appl Math & Comp Sci DK-2800 Copenhagen Denmark Tsinghua Univ Res Inst Informat Technol RIIT Beijing 100084 Peoples R China Univ New Brunswick Fac Comp Sci Fredericton NB E3B 5A3 Canada

Secure aggregation based on masked encryption is a crucial technique for data collection in the Internet of Things (IoT) as it employs a lightweight style to enable global data aggregation while protecting individual data. However, network instability makes the design of such schemes more complex as dropout-resiliency is required, where the overheads substantially increase with growing dropped users. Moreover, existing methods primarily concentrate on aggregation and fail to support complex data analysis, such as histogram estimation. This paper proposes a Robust and Lightweight data aggregation (RLDA) scheme in edge-cloud systems. RLDA leverages the offline/online paradigm to achieve robust data aggregation, where edge nodes are introduced to assist verifiable key generation offline and data aggregation and key recovery online. RLDA decouples keys of dropped and surviving users so that it can reduce the overhead by always recovering the keys of surviving users rather than reconstructing the keys of growing dropped users. To achieve secure histogram estimation, we design two recoverable aggregation algorithms that support the transformation between vector and single value, and additionally support multidimensional data aggregation. We prove the security and dropout-resiliency of RLDA. The performance shows that RLDA significantly reduces the overhead with growing dropped users.

关键词： Encryption Histograms Estimation data aggregation Internet of Things Differential privacy Privacy Robust aggregation Secure histogram estimation multidimensional data aggregation Edge computing

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An Inherent Difficulty in the aggregation of multidimensional data

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2020年第3期28卷 602-606页

作者： Gagolewski, Marek Perez-Fernandez, Raul De Baets, Bernard Warsaw Univ Technol Fac Math & Informat Sci PL-00662 Warsaw Poland Polish Acad Sci Syst Res Inst PL-01447 Warsaw Poland Univ Ghent Dept Data Anal & Math Modelling Res Unit KER MIT B-9000 Ghent Belgium

In the field of information fusion, the problem of data aggregation has been formalized as an order-preserving process that builds upon the property of monotonicity. However, fields such as computational statistics, data analysis, and geometry usually emphasize the role of equivariances to various geometrical transformations in aggregation processes. Admittedly, if we consider a unidimensional data fusion task, both requirements are often compatible with each other. Nevertheless, in this paper, we show that, in the multidimensional setting, the only idempotent functions that are monotone and orthogonal equivariant are the over-simplistic weighted centroids. Even more, this result still holds after replacing monotonicity and orthogonal equivariance by the weaker property of orthomonotonicity. This implies that the aforementioned approaches to the aggregation of multidimensional data are irreconcilable, and that, if a weighted centroid is to be avoided, we must choose between monotonicity and a desirable behavior with regard to orthogonal transformations.

关键词： Aggregates data aggregation data analysis Geometry data integration Task analysis Centroid monotonicity multidimensional data aggregation orthogonal equivariance

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A Multi-dimensional data aggregation Signcryption Scheme with Anonymity in the Smart Grid 7th

A Multi-dimensional Data Aggregation Signcryption Scheme wit...

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7th International Conference on Frontiers in Cyber Security

作者： Liu, Xukai Zhang, Wanju Yang, Qiansi Liu, Xintao Han, Gang Liu, Shuanggen Xian Univ Posts & Telecommun Sch Cyberspace Secur Xian Shaanxi Peoples R China

ISBN: (纸本)9789819601530;9789819601547

Under the background of smart grid, the regular collection of electricity data from customers using smart meters presents a major risk of privacy leakage. Moreover, conventional multidimensional data aggregation schemes rely on trusted authorities, which are unable to withstand joint attacks from gateways and control centers, leading to concerns about privacy violations among users. To address this issue, we propose a multidimensional data aggregation signcryption scheme with anonymity in smart grid. By integrating the Paillier homomorphic encryption system and fog computing architecture, we achieve authentication and key agreement between users to ensure data privacy and resist potential attacks from control centers(CC) and fog nodes(FN). The solution integrates the Home Energy Management System (HEMS) with the power company's billing department Management System (MS) to enhance intelligence and management efficiency. Furthermore, security analysis and performance assessment demonstrate that our proposed scheme surpasses existing techniques in terms of security and efficiency.

关键词： multidimensional data aggregation Paillier homomorphic encryption Smart grid privacy preserving

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Edge Computing Based Privacy-Preserving data aggregation Scheme in Smart Grid 39

Edge Computing Based Privacy-Preserving Data Aggregation Sch...

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39th IEEE International Performance Computing and Communications Conference (IPCCC)

作者： Kang, Yuhao Guo, Songtao Li, Pan Yang, Yuanyuan Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China SUNY Stony Brook Dept Elect & Comp Engn Stony Brook NY 11794 USA

ISBN: (纸本)9781728198293

Smart grid is a highly intelligent power system integrating advanced communication technology, sensor measurement and automatic control technology, which is gradually replacing the traditional power grid. However, the smart grid faces challenges of balancing privacy, efficiency and functionality when processing massive amount of data. In this paper, a smart grid model based on edge computing paradigm is established, and then an efficient privacy-preserving multidimensional data aggregation scheme is proposed. This scheme adopts an improved identity-based signature algorithm and Paillier homomorphic cryptosystem to protect the privacy of users. In addition, super-increasing sequence is used in the proposed scheme to enable smart meters to report multiple types of data in a single reporting message, so that the Service Center (SC) can perform one-way analysis of variance on the data to provide users with more personalized services. Also the security analysis indicates that the proposed scheme works in protecting user's electricity consumption privacy. Finally, performance analyses indicate that this scheme can effectively reduce the computational overhead.

关键词： Smart grid Edge computing Homomorphic cryptosystem multidimensional data aggregation Privacy-preserving

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Efficient Privacy-Preserving Multi-Dimensional data aggregation Scheme in Smart Grid

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IEEE ACCESS 2019年 7卷 32907-32921页

作者： Ming, Yang Zhang, Xuanyi Shen, Xiaoqin Changan Univ Sch Informat Engn Xian 710064 Shaanxi Peoples R China Xian Univ Technol Sch Sci Xian 710054 Shaanxi Peoples R China

Smart grid, characterized by high efficiency, security, and flexibility, is gradually replacing the traditional power grid. data aggregation technology is frequently used to avoid user privacy disclosure as a result of power consumption data transmission in the smart grid. However, traditional one-dimensional data aggregation schemes fail to meet the demands of fine-grained analysis. Therefore, this paper proposes an efficient privacy-preserving multi-dimensional data aggregation (P(2)MDA) scheme in smart grid by virtue of homomorphic encryption and superincreasing sequence. The security analysis indicates that the proposed scheme is proved to be secure in the random oracle model, satisfying all security and privacy requirements. The extensive performance analysis shows that in comparison to the related schemes, the proposed scheme achieves lowest computation and communication costs, thus appropriate for practical applications.

关键词： Smart grid multidimensional data aggregation homomorphic encryption privacy preserving

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Penalty-based aggregation of multidimensional data

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FUZZY SETS AND SYSTEMS 2017年 325卷 4-20页

作者： Gagolewski, Marek Polish Acad Sci Syst Res Inst Ul Newelska 6 PL-01447 Warsaw Poland Warsaw Univ Technol Fac Math & Informat Sci Ul Koszykowa 75 PL-00662 Warsaw Poland

Research in aggregation theory is nowadays still mostly focused on algorithms to summarize tuples consisting of observations in some real interval or of diverse general ordered structures. Of course, in practice of information processing many other data types between these two extreme cases are worth inspecting. This contribution deals with the aggregation of lists of data points in R-d for arbitrary d >= 1. Even though particular functions aiming to summarize multidimensional data have been discussed by researchers in data analysis, computational statistics and geometry, there is clearly a need to provide a comprehensive and unified model in which their properties like equivariances to geometric transformations, internality, and monotonicity may be studied at an appropriate level of generality. The proposed penalty-based approach serves as a common framework for all idempotent information aggregation methods, including componentwise functions, pairwise distance minimizers, and data depth-based medians. It also allows for deriving many new practically useful tools. (C) 2016 Elsevier B.V. All rights reserved.

关键词： multidimensional data aggregation Penalty functions data depth Centroid Median

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