Multidimensional Data Aggregation Research Articles

Multidimensional Epidemiological Survey Data Aggregation Scheme Based on Personalized Local Differential Privacy

In recent years, with the rapid development of intelligent technology, information security and privacy issues have become increasingly prominent. Epidemiological survey data (ESD) research plays a vital role in understanding the laws and trends of disease transmission. However, epidemiological investigations (EI) involve a large amount of privacy-sensitive data which, once leaked, will cause serious harm to individuals and society. Collecting EI data is also a huge task. To solve these problems and meet personalized privacy protection requirements in EIs, we improve the uOUE protocol based on utility-optimized local differential privacy to improve the efficiency and accuracy of data coding. At the same time, aiming at the collection and processing of ESD, a multidimensional epidemiological survey data aggregation scheme based on uOUE is designed. By using Paillier homomorphic encryption and an identity-based signature scheme to further prevent differential attacks and achieve multidimensional data aggregation, the safe, efficient, and accurate aggregation processing of ESD is executed. Through security proof and performance comparison, it is verified that our algorithm meets the requirements of local differential privacy and unbiased estimation. The experimental evaluation results on two data sets show that the algorithm has good practicability and accuracy in ESD collection and provides reliable and effective privacy protection.

MDA-FLH: Multidimensional Data Aggregation Scheme With Fine-Grained Linear Homomorphism for Smart Grid

MDA-FLH: Multidimensional Data Aggregation Scheme With Fine-Grained Linear Homomorphism for Smart Grid

A Lightweight and Robust Multidimensional Data Aggregation Scheme for IoT

A Lightweight and Robust Multidimensional Data Aggregation Scheme for IoT

Lightweight and Verifiable Secure Aggregation for Multi-dimensional Data in Edge-enhanced IoT

The development of IoT applications promotes the generation of large-scale data. In this situation, more and more organizations or institutions are interested in collecting and analyzing these data to obtain high-quality information to serve people’s lives. Nevertheless, existing secure aggregation solutions are mostly proposed under the honest-but-curious threat model, without considering malicious aggregators. Actually, the aggregation node may return tampered or forged aggregation results for vulnerabilities or ulterior motives. It is more challenging to devise efficient verifiable data aggregation due to the characteristics of IoT devices. In this paper, we propose a Lightweight and Verifiable Secure Aggregation Scheme for Multi-dimensional Data in Edge-enhanced IoT (LVSA-MD), which allows the secure aggregation of multi-dimensional data, and the requester can verify the correctness and integrity of the aggregation results. In LVSA-MD, we combine Chinese remainder theorem with secret sharing technique to protect the individual data privacy, as well as reducing the communication overhead. Moreover, by involving homomorphic MAC, we achieve a lightweight verifiable mechanism to ensure the integrity of result. We additionally employ identity-based signature to achieve the source authentication. Detailed security analysis demonstrates that the proposed LVSA-MD achieves the goal of protecting privacy, integrity and authentication. Moreover, extensive theoretical analyses and experimental evaluations show that our LVSA-MD performs efficiently with respect to computation and communication while retaining more desired properties.

A Multidimensional Data Aggregation Scheme of Smart Home in Microgrid With Fault Tolerance and Billing for Demand Response

A Multidimensional Data Aggregation Scheme of Smart Home in Microgrid With Fault Tolerance and Billing for Demand Response

A certificateless multi-dimensional data aggregation scheme for smart grid

In the context of the smart grid, the use of smart meters to regularly collect electricity data from customers poses a significant risk of privacy disclosure. Furthermore, traditional data aggregation schemes assume a fully trusted authority, neglecting the possibility of malicious trusted third parties and collusive attacks from control centers and gateways, which raises privacy invasion concerns for users. To address this issue, this article proposes a multi-dimensional data aggregation scheme based on certificateless public key cryptography for smart grid, which utilizes Paillier homomorphic encryption system in a fog computing-based architecture and implements authentication and key negotiation among member users. This scheme is designed to resist collusive attacks from control centers (CC) and fog nodes (FNs), while also protecting users’ privacy even in the face of a malicious key generation center (KGC). Detailed security analysis demonstrates that the proposed scheme meets the security requirements of the smart grid. Additionally, Performance analysis shows that the proposed scheme can effectively reduce the computational overhead of both smart meters and aggregators compared to existing multidimensional data aggregation schemes.

SEMDA: Secure and Efficient Multidimensional Data Aggregation in Smart Grid without a Trusted Third Party

Smart grids are a combination of traditional power system engineering as well as information and communications technology. Smart grid terminals provide convenient services to users by aggregating their data in real time. However, terminals can derive user privacy information from real-time data on smart devices. Therefore, security data aggregation has been widely studied in the field of smart grid. Most existing schemes are one-dimensional data aggregation or rely on a trusted third party. In reality, multidimensional data (such as a user’s electricity consumption or user’s main usage time, etc.) makes sense for terminals to flexibly adjust supply and demand strategies. In this paper, we propose an efficient and secure multidimensional data aggregation scheme that supports batch validation without a trusted third party. Firstly, we apply the Chinese remainder theorem to encode the user’s multidimensional data and realize the independence of each dimension in terminal decryption. Secondly, we adopt a secure key negotiation protocol that does not require a trusted third party. Finally, based on paillier homomorphic encryption and bilinear pairing, we construct an encryption scheme that can reuse the key and blind factor and support batch verification. The analysis results show that our scheme is secure for users’ privacy protection. Experimental results show that, compared with existing 1 dimensional aggregation schemes, our scheme has almost no growth in computational overhead for terminal decryption.

An Efficient and Robust Multidimensional Data Aggregation Scheme for Smart Grid Based on Blockchain

In order to analyze real-time power data without revealing users’ privacy, privacy-preserving data aggregation schemes have been extensively researched in smart grid. However, most of the existing schemes either can only allow stationary users, or require a trusted center. In this paper, we propose an efficient and robust multidimensional data aggregation scheme based on blockchain. In our scheme, a leader election algorithm in Raft protocol is used to select a mining node from all smart meters to aggregate data. A dynamically verifiable secret sharing homomorphism scheme is adopted to realize flexible dynamic user management. In addition, our scheme can not only resist internal and external attacks but also support multidimensional data aggregation and fault tolerance. The security analysis shows that our proposed scheme is IND-CPA secure and can meet stronger security features. The experimental results show that compared with other schemes, our scheme can be implemented with lower computation and communication overhead.

A privacy-preserving multidimensional data aggregation scheme with secure query processing for smart grid

A privacy-preserving multidimensional data aggregation scheme with secure query processing for smart grid

Privacy-Preserving Outsourcing Algorithms for Multidimensional Data Encryption in Smart Grids.

With the development of the Internet of Things, smart grids have become indispensable in our daily life and can provide people with reliable electricity generation, transmission, distribution and control. Therefore, how to design a privacy-preserving data aggregation protocol has been a research hot-spot in smart grid technology. However, these proposed protocols often contain some complex cryptographic operations, which are not suitable for resource-constrained smart meter devices. In this paper, we combine data aggregation and the outsourcing of computations to design two privacy-preserving outsourcing algorithms for the modular exponentiation operations involved in the multi-dimensional data aggregation, which can allow these smart meter devices to delegate complex computation tasks to nearby servers for computing. By utilizing our proposed outsourcing algorithms, the computational overhead of resource-constrained smart meter devices can be greatly reduced in the process of data encryption and aggregation. In addition, the proposed algorithms can protect the input’s privacy of smart meter devices and ensure that the smart meter devices can verify the correctness of results from the server with a very small computational cost. From three aspects, including security, verifiability and efficiency, we give a detailed analysis about our proposed algorithms. Finally, through carrying out some experiments, we prove that our algorithms can improve the efficiency of performing the data encryption and aggregation on the smart meter device side.

An Efficient Multi-dimensional Data Aggregation Scheme for Fog-Assisted Wireless Body Area Network

The rapid development of wireless body area network and monitoring technology has brought new opportunities for the later rehabilitation of patients. Due to the limited computing power and power of wireless body area network (WBAN) sensor nodes, combining WBAN with fog computing is one of the feasible solutions for remote monitoring. Wireless body area network can exchange medical information in real time, which is an effective way to improve medical services. The WBAN is inefficient and vulnerable to attacks, leading to leakage of user privacy information. This paper proposes an efficient data aggregation scheme for fog-assisted WBAN. The analysis shows that the scheme can resist the existing attack. The security analysis shows that the scheme meets the privacy and security requirements of data aggregation in WBAN. Compared with existing schemes, this scheme has an efficiency advantage.

EPPSA: Efficient Privacy-Preserving Statistical Aggregation Scheme for Edge Computing-Enhanced Wireless Sensor Networks

In edge computing-enhanced wireless sensor networks (WSNs), multidimensional data aggregation can optimize the utilization of computation resources for data collection. How to improve the efficiency of data aggregation has gained considerable attention in both academic and industrial fields. This article proposes a new efficient privacy-preserving statistical aggregation scheme (EPPSA) for WSNs, in which statistical data can be calculated without exposing the total number of sensor devices to control center. The EPPSA scheme supports multiple statistical aggregation functions, including arithmetic mean, quadratic mean, weighted mean, and variance. Furthermore, the EPPSA scheme adopts the modified Montgomery exponentiation algorithms to improve the aggregation efficiency in the edge aggregator. The performance evaluation shows that the EPPSA scheme gets higher aggregation efficiency and lower communication load than the existing statistical aggregation schemes.

EPMDA-FED: Efficient and Privacy-Preserving Multidimensional Data Aggregation Scheme With Fast Error Detection in Smart Grid

Smart grids bring advantages of reliabilty 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 securtiy 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 paper. Firstly, we adopt Chinese Remainder Theorem to pack multidimensional data and encrypt the processed data using the keys generated by the negotiation among users and the control center. Secondly, our scheme is efficient for encryption without high-cost additive homomorphic public key encryption scheme, such as Paillier cryptosystem and supporting batch verification with fast error detection. Our proposed error detection algorithm is quite efficient with sublinear computation 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 existing competing approaches in terms of computation and communication overhead.

Aggregation of multidimensional data for the decision support process for the management of microgrids with renewable energy sources

The object of research is the process of processing and storing data when making decisions on managing the life cycle of electricity generation and consumption in microgrids with renewable energy sources. The prospects of the study are due to the fact that in order to provide a full-fledged decision support process in the management of microgrids with renewable energy sources, it is necessary to consolidate and manipulate multidimensional data in multithreading and online information processing. To solve the problem, the theoretical methods of analysis, abstraction, induction and deduction were used. To ensure multidimensionality and multithreading of data processing, it is proposed to develop a data warehouse based on the snowflake data model. Efficiency of information processing in real time is provided by an operational database built on the principle of OLTP. The organization of the joint work of the data warehouse with the operational database, the consolidation and manipulation of data is provided by triggers. The result of the work is a data warehouse that will be used in the decision support system for managing energy microgrids, which will improve the efficiency of data processing and storage. This is achieved by combining the work of a centralized data warehouse with an operational database, as well as the use of a separate data mart for each user of the system. The practical significance of the work lies in the fact that the data warehouse will become part of the decision support system for processing information about the life cycle of energy in the management of energy infrastructure. Compared to using a single database for a decision support system, this approach ensures the speed of working with data and allows differentiating between the use of a data warehouse for analytics and data manipulation operations. The data warehouse was deployed in a cloud environment on the Amazon Web Services (AWS) platform and the Amazon Relational Database Service (Amazon RDS) web service. Secure access to client data is implemented using data marts.

An Efficient Privacy-Preserving Aggregation Scheme for Multidimensional Data in IoT

Internet of Things (IoT) enables terminal devices connecting with the Internet and provides various intelligent applications by analyzing devices data. As a typical IoT technique, edge computing provides a three-tier architecture to reduce communications and improve efficiency. Specifically, edge nodes are responsible for collecting and aggregating device data, and then send processed results to the cloud for subsequent analysis. However, the data aggregation function will compromise the privacy of device data. In this article, we proposed an efficient privacy-preserving multidimensional data aggregation scheme for IoT, called PMDA. The scheme uses the Chinese remainder theorem to design a homomorphic encryption method that encryptes a multiple-dimensional small integer vector into one ciphertext and keeps linear homomorphic properties per dimension. Combining with the signature mechanism and the batch verification method, the scheme guarantees nonrepudiation of device data and enhance verification efficiency at edge nodes. Through theoretical analysis, we demonstrate that the proposed scheme can achieve correctness, privacy, authentication, and integrity. After performance evaluation, we demonstrate that our scheme is superior to other schemes in terms of computation and communication costs. In particular, as the message dimension increases, our scheme computation costs almost a tenth of others at the 80-bits security level.

Non-Interactive and Secure Data Aggregation Scheme for Internet of Things

The popularity of mobile devices in Internet of Things has brought great convenience to the lives of the people. Massive data generated in the IoT are outsourced and stored on cloud platforms so that data aggregation and analysis can be performed on the massive data. However, these data often contain sensitive information of mobile devices, so effective protection of mobile user privacy is the primary condition for further development of IoT. Most of the current data aggregation schemes require a lot of interactions between users, and thus this paper designs a non-interactive secure multidimensional data aggregation scheme. This scheme adopts an additive secret sharing technique to mask the shared data and send it to two non-colluding servers, and then the servers aggregate the ciphertext respectively. Different from the existing schemes, our proposed scheme achieves non-interaction between users, and the aggregation result is kept confidential to the server and supports mobile users offline. Finally, we perform an experimental evaluation which proves the effectiveness of our scheme.

LPM2DA: a lattice-based privacy-preserving multi-functional and multi-dimensional data aggregation scheme for smart grid

LPM2DA: a lattice-based privacy-preserving multi-functional and multi-dimensional data aggregation scheme for smart grid

Multifunctional and Multidimensional Secure Data Aggregation Scheme in WSNs

In wireless sensor networks (WSNs), data aggregation (DA) has become one of the most practical techniques to reduce processing delay and improve energy efficiency. To support intelligent applications, sensor nodes need to report heterogeneous and diverse data, which induce the demand for multidimensional DA and multifunctional data analysis. To solve the current security problems and functional requirements, we propose a multifunctional and multidimensional secure DA scheme to strike the balance between data availability and privacy. First, we design a Chinese remainder theorem conversion method with the counter to encode multidimensional data into large integers, which can be operated by linear homomorphic encryption schemes. Then, we introduce a multifunctional data analysis method supporting diversified aggregation functions, including linear, polynomial, and continuous functions. Moreover, we demonstrate that the proposed scheme can achieve confidentiality, integrity, authentication, and resistance against false data injection attacks. The experimental results show that the supported max dimension of one ciphertext in our scheme is at least twice that of existing schemes. Thus, in scenarios with high dimensions, our scheme is superior to the existing schemes in terms of computation and communication costs.

PLDP: Personalized Local Differential Privacy for Multidimensional Data Aggregation

The collection of multidimensional crowdsourced data has caused a public concern because of the privacy issues. To address it, local differential privacy (LDP) is proposed to protect the crowdsourced data without much loss of usage, which is popularly used in practice. However, the existing LDP protocols ignore users’ personal privacy requirements in spite of offering good utility for multidimensional crowdsourced data. In this paper, we consider the personality of data owners in protection and utilization of their multidimensional data by introducing the notion of personalized LDP (PLDP). Specifically, we design personalized multiple optimized unary encoding (PMOUE) to perturb data owners’ data, which satisfies ϵ total -PLDP. Then, the aggregation algorithm for frequency estimation on multidimensional data under PLDP is developed, which is described in two situations. Experiments are conducted on four real datasets, and the results show that the proposed aggregation algorithm yields high utility. Moreover, case studies with four real datasets demonstrate the efficiency and superiority of the proposed scheme.

A Privacy-preserving Multi-dimensional Data Aggregation Scheme with Forward Security in Smart Grid

Smart grid is a new generation of power system on the strength of rapid-speed bidirectional communication network. In smart grid, the electricity data from users needs to be collected to realize efficient energy management, which may reveal their privacy. In the past period of time, data aggregation protocols have been extensively studied to solve this problem. However, these protocols treat users’ electricity data as single-dimensional data, which is inconsistent with reality. Moreover, key leakage is also a serious security threat in the smart grid. Therefore, we propose a multi-dimensional data aggregation protocol with forward security. In this protocol, a cube data structure is designed to represent the multi-dimensional electricity consumption data of users in a region over multiple time periods, realizing the dual aggregation of electricity data both in time and space. In addition, security analysis shows that the proposed protocol satisfies multiple security properties and performance analysis shows that our protocol is more superior in the smart grid environment.