Low Communication Overhead Research Articles

Privacy-preserving Indoor Localization in Cloud Environments Based on Ranging Transformation and Inner Product Encryption

Abstract. Cloud-based indoor positioning services offer advantages over non-cloud approaches, but also face serious privacy concerns. How to utilize an untrusted cloud server for location computation while not allowing the server to obtain the localization results is the most challenge in solving the privacy concerns, which has not been solved by the existing research. In this paper, a privacy-preserving indoor positioning scheme was designed to address this challenge. Based on the previous work using Inner Product Encryption for the protection of ranging information and anchor location information during the localization process, a transformation method was additionally proposed for the protection of localization results. The ranging information was transformed by the target, which enables the localization server to get only the transformed localization result, and only the target can recover the real location from it. In addition, this transformation is designed to be performed on the ciphertexts of the Inner Product Encryption so that the private information required for transformation is in the ciphertext form thus avoiding privacy leakage. Theoretical analysis and experimental results demonstrated that this scheme can protect the ranging information, localization results and anchor location information. At the same time, it has lower computation and communication overhead and hardly degrades the localization accuracy.

BIAS: A novel secure and efficient biometric-based anonymous authentication scheme

Currently, biometric-based authentication schemes are widely adopted in the field of online payments. Consequently, this has led to an increasing number of people becoming concerned about the privacy-preservation of their biometric data. Gunasinghe et al. presented PrivBioMTAuth, a solution for mobile phone biometric-based authentication designed to protect users’ privacy. However, the solution has drawbacks, such as its impact on the execution efficiency of the authentication protocol or its vulnerability to man-in-the-middle attacks during the authentication phase. Moreover, the user’s biometric image and the password must be revealed to the identity provider, which may raise security concerns. In this work, we present a novel secure and efficient biometric-based anonymous authentication solution with fully succinct verification and significantly lower storage and communication overhead. Different from PrivBioMTAuth, we rely on the NIZK argument given in Groth’s work to reduce the size of the anonymous identity and simplify the verification complexity. In addition, we design a high-performance protocol for conducting large-scale verification of the user’s anonymous identities. We propose an optimized multi-exponentiation argument based on Bayer et al.’s work and utilize it to ensure that a semi-trusted identity provider who seeks to access the users’ sensitive biometric information can faithfully execute the users’ identity registration protocol. The experiment results show that our proposed scheme is efficient and has privacy-preserving capabilities, and it can be applied in the resource-constrained devices.

Blockchain-Based Cold Chain Traceability with NR-PBFT and IoV-IMS for Marine Fishery Vessels

Due to limited communication, computing resources, and unstable environments, traditional cold chain traceability systems are difficult to apply directly to marine cold chain traceability scenarios. Motivated by these challenges, we construct an improved blockchain-based cold chain traceability system for marine fishery vessels. Firstly, an Internet of Vessels system based on the Iridium Satellites (IoV-IMS) is proposed for marine cold chain monitoring. Aiming at the problems of low throughput, long transaction latency, and high communication overhead in traditional cold chain traceability systems, based on the Practical Byzantine Fault Tolerance (PBFT) consensus algorithm, a Node-grouped and Reputation-evaluated PBFT (NR-PBFT) is proposed to improve the reliability and robustness of blockchain system. In NR-PBFT, an improved node grouping scheme is designed, which introduces a consistent hashing algorithm to divide nodes into consensus and candidate sets, reducing the number of nodes participating in the consensus process, to lower communication overhead and transaction latency. Then, a reputation evaluation model is proposed to improve the node selection mechanism of NR-PBFT. It enhances the enthusiasm of nodes to participate in consensus, which considers the distance between fishery vessels, data size, and refrigeration temperature factors of nodes to increase throughput. Finally, we carried out experiments on marine fishery vessels, and the effectiveness of the cold chain traceability system and NR-PBFT were verified. Compared with PBFT, the transaction latency of NR-PBFT shortened by 81.92%, the throughput increased by 84.21%, and the communication overhead decreased by 89.4%.

Unlinkable and Revocable Signcryption Scheme for VANETs

Vehicular ad-hoc networks (VANETs) can significantly improve the level of urban traffic management. However, the sender unlinkability has become an intricate issue in the field of VANETs’ encryption. As the sender signcrypts a message, the receiver has to use the sender’s identity or public key to decrypt it. Consequently, the sender can be traced using the same identity or public key, which poses some security risks to the sender. To address this issue, we present an unlinkable and revocable signcryption scheme (URSCS), where an efficient and powerful signcryption mechanism is adopted for communication. The sender constructs a polynomial to generate a unique session key for each communication, which is then transmitted to a group of receivers, enabling the same secret message to be sent to multiple receivers. Each time a secret message is sent, a new key pair is generated, and an anonymization mechanism is introduced to conceal the true identity of the vehicle, thus preventing malicious attackers from tracing the sender through the public key or the real identity. With the introduction of the identification public key, this scheme supports either multiple receivers or a single receiver, where the receiver can be either road side units (RSUs) or vehicles. Additionally, a complete revocation mechanism is constructed with extremely low communication overhead, utilizing the Chinese remainder theorem (CRT). Formal and informal security analyses demonstrate that our URSCS scheme meets the expected security and privacy requirements of VANETs. The performance analysis shows that our URSCS scheme outperforms other represented schemes.

Design and Implementation of Lightweight Certificateless Secure Communication Scheme on Industrial NFV-Based IPv6 Virtual Networks

With the fast growth of the Industrial Internet of Everything (IIoE), computing and telecommunication industries all over the world are moving rapidly towards the IPv6 address architecture, which supports virtualization architectures such as Network Function Virtualization (NFV). NFV provides networking services like routing, security, storage, etc., through software-based virtual machines. As a result, NFV reduces equipment costs. Due to the increase in applications on Industrial Internet of Things (IoT)-based networks, security threats have also increased. The communication links between people and people or from one machine to another machine are insecure. Usually, critical data are exchanged over the IoE, so authentication and confidentiality are significant concerns. Asymmetric key cryptosystems increase computation and communication overheads. This paper proposes a lightweight and certificateless end-to-end secure communication scheme to provide security services against replay attacks, man-in-the-middle (MITM) attacks, and impersonation attacks with low computation and communication overheads. The system is implemented on Linux-based Lubuntu 20.04 virtual machines using Java programming connected to NFV-based large-scale hybrid IPv4-IPv6 virtual networks. Finally, we compare the performance of our proposed security scheme with existing schemes based on the computation and communication costs. In addition, we measure and analyze the performance of our proposed secure communication scheme over NFV-based virtualized networks with regard to several parameters like end-to-end delay and packet loss. The results of our comparison with existing security schemes show that our proposed security scheme reduces the computation cost by 38.87% and the communication cost by 26.08%.

A comprehensive and secure scheme for privacy-preserving smart meter data aggregation in the smart grid

Smart meters play a crucial role in the functioning of the smart grid by rapidly collecting and transmitting power consumption data to electricity companies. However, the real-time nature of smart meter data poses privacy risks for customers. To address this concern, encrypted aggregation of smart meter power consumption has been widely employed to protect customer privacy. In this paper, we propose an innovative scheme designed specifically for smart grids to fulfill these requirements. Our scheme demonstrates superior performance compared to existing solutions in terms of communication cost, computation, and functionality features. The proposed authentication protocol not only enables the secure sharing of power consumption data but also satisfies various security requirements, including mutual authentication, anonymity, prevention of man-in-the-middle attacks, and more. Furthermore, our framework exhibits significantly lower computing, communication, and storage overhead compared to similar schemes in the context of smart grids. This highlights the comprehensive and secure nature of our suggested framework, surpassing other existing smart grid schemes in terms of overall effectiveness and reliability.

A Novel Localized Fast Multipole Method for Computations With Spatially Correlated Observation Error Statistics in Data Assimilation

AbstractSeveral observation types (e.g., geostationary satellite and Doppler radar observations) have recently been found to exhibit strong spatial error correlations. Including these error statistics in data assimilation for numerical weather prediction can improve analysis quality and forecast skill. Moreover, it allows for increases in the spatial density of observations assimilated, which is needed for the provision of information on appropriate scales for high‐resolution forecasting. However, introducing correlated error statistics may increase the computational complexity and parallel communication costs of matrix‐vector products involving observation precision matrices (inverse observation error covariance matrices). Without new approaches, we cannot take full advantage of new observation uncertainty estimates. We develop a new numerical approximation method based on a particular type of fast multipole method and a domain localization approach. The basic idea is to divide the observation domain into boxes and then separate calculations of matrix‐vector products according to the partition. These calculations can be done in parallel with very low communication overheads. The new method is easy to implement and parallelize, and it is applicable to a wide variety of observation precision matrices. We applied the new method to a simple variational data assimilation problem and found that the computational cost of the variational minimization was dramatically reduced while preserving analysis accuracy across a range of scales. The new method has the potential to be used as an efficient technique for practical applications where a large number of observations with mutual error correlations need to be assimilated quickly.

Federated Learning via Plurality Vote.

Federated learning allows collaborative clients to solve a machine-learning problem while preserving data privacy. Recent studies have tackled various challenges in federated learning, but the joint optimization of communication overhead, learning reliability, and deployment efficiency is still an open problem. To this end, we propose a new scheme named federated learning via plurality vote (FedVote). In each communication round of FedVote, clients transmit binary or ternary weights to the server with low communication overhead. The model parameters are aggregated via weighted voting to enhance the resilience against Byzantine attacks. When deployed for inference, the model with binary or ternary weights is resource-friendly to edge devices. Our results demonstrate that the proposed method can reduce quantization error and converges faster compared to the methods directly quantizing the model updates.

Blockchain-assisted full-session key agreement for secure data sharing in cloud computing

Data sharing in cloud computing allows multiple data owners to freely share their data resources while security and privacy issues remain inevitable challenges. As a foundation of secure communication, authenticated key agreement (AKA) scheme has been recognized as a promising approach to solve such problems. However, most existing AKA schemes are based on the cloud-based architecture, privacy and security issues will inevitably occur once the centralized authority is attacked. Besides, most previous schemes require an online registration authority for authentication, which may consume significant resources. To address these drawbacks, for secure data sharing in cloud computing, a blockchain-assisted full-session key agreement scheme is proposed. After the registration phase, the registration authority does not engage in authentication and key agreement process. By utilizing blockchain technology, a common session key between the remote user and cloud server can be negotiated, and a shared group key among multiple remote users can be negotiated without private information leakage. Formal and informal security proof demonstrated the proposed scheme is able to meet the security and privacy requirements. The detail performance evaluation shows that the proposed scheme has lower computation costs and acceptable communication overheads while superior security is ensured.

Enhancing security and privacy in smart agriculture: A novel homomorphic signcryption system

Technology driven Smart Agriculture is transforming modern day farming practices through digital tools and real-time data analysis. It monitors plant health in both soil and soilless cultivation, promoting environmentally responsible practices by utilizing integrated sensors and precision methods. Despite the significant potential benefits, challenges persist in ensuring data security in the smart agriculture frameworks. The existing research has explored various mechanisms to cater these issues, yet these solutions susceptible to known attacks i.e., Confidentiality, Authentication, Privacy preservation. Notably, the available schemes in open literature face performance deficiencies due to an overreliance on Rivest-Shamir-Adleman (RSA), bilinear pairing, and elliptic curve (EC). To comprehensively address these challenges, this study presents a novel approach via employing homomorphic Signcryption based on Hyper-Elliptic Curves (HEC). Our HEC based Signcryption technique enhances security with lower computational cost and communication overhead as compared to the existing techniques. Furthermore, our solution provides the best performance in terms of efficiency, addressing the privacy preservation issues with conformance to the (Confidentiality, Integrity, and Availability) CIA triad. For proof of concept and correctness, we have validated the results in Scyther tool, with the assurance that proposed scheme is correct, verified, and resistant against known set of attacks. The proposed scheme achieves a remarkable reduction in both computational and communication costs around 95.08 % and 89.28 % respectively, enhancing practicality and suitability for real-world implementation. Thus, making it a suitable candidate for employing in smart agriculture environments, encompassing both soil and soilless cultivation scenarios. This research contributes a robust, secure, cost effective, and efficient solution to the challenges posed by the evolving landscape of agriculture, paving the way for enhanced data protection and system resilience in smart agriculture systems.

Certificateless anti-quantum blind signcryption for e-cash

Electronic cash (e-cash) has become major payment way in the era of internet of things. However, the leakage of user privacy and transaction data is becoming more and more serious in e-cash system. To solve these problems, we construct a new certificateless blind signcryption from NTRU lattice (NTRUL-CLBSC) suitable for e-cash. NTRUL-CLBSC has low communication overhead and high calculation efficiency because of small storage space and short key size in number theory research unit (NTRU) lattice. Based on RSIS and DNCC problems in NTRU lattice, NTRUL-CLBSC can guarantee the anti-quantum, anti-forgery, confidentiality and blindness, and there is no key escrow and certificate management in e-cash application.

A Provably Secure Anonymous Authentication Protocol for Consumer and Service Provider Information Transmissions in Smart Grids

Smart grids integrate information technology, decision support systems, communication networks, and sensing technologies. All these components cooperate to facilitate dynamic power adjustments based on received client consumption reports. Although this brings forth energy efficiency, the transmission of sensitive data over the public internet exposes these networks to numerous attacks. To this end, numerous security solutions have been presented recently. Most of these techniques deploy conventional cryptographic systems such as public key infrastructure, blockchains, and physically unclonable functions that have either performance or security issues. In this paper, a fairly efficient authentication scheme is developed and analyzed. Its formal security analysis is carried out using the Burrows–Abadi–Needham (BAN) logic, which shows that the session key negotiated is provably secure. We also execute a semantic security analysis of this protocol to demonstrate that it can resist typical smart grid attacks such as privileged insider, guessing, eavesdropping, and ephemeral secret leakages. Moreover, it has the lowest amount of computation costs and relatively lower communication overheads as well as storage costs.

Improved RSA dynamic cryptographic accumulator-based anonymous batch authentication scheme for Internet of Vehicles

The Internet of Vehicles (IoV) provides various services to vehicles through information sharing to ensure road safety and improve traffic efficiency. However, malicious attackers can challenge the privacy and security systems of the IoV by conducting security attacks. Currently, many schemes use bilinear pairing or elliptic curves to construct authentication systems, ensuring anonymity of vehicle identities and message security. However, these schemes suffer drawbacks such as low computational efficiency and high communication overhead in highly dynamic IoV. Therefore, we propose a new efficient certificateless message authentication scheme. The proposed scheme constructs a lightweight security authentication protocol by improving the RSA dynamic accumulator and combining it with non-interactive discrete logarithm zero-knowledge proofs. In addition, our scheme eliminates the need for bilinear pairing operations, thereby reducing computational overhead. Simultaneously, it enables real-time tracking and revocation of malicious vehicle identities. Security analysis shows that our scheme can be reduced to a secure S-RSA assumption under the random oracle model, meeting various security requirements of the IoV. Performance analysis shows that our scheme diminishes not only the computational overhead but also the communication overhead.

An Efficient Lightweight Authentication Scheme for Smart Meter

With the rapid development of the information age, smart meters play an important role in the smart grid. However, there are more and more attacks on smart meters, which mainly focus on the identity authentication of smart meters and the security protection of electricity consumption data. In this paper, an efficient lightweight smart meter authentication scheme is proposed based on the Chinese Remainder Theorem (CRT), which can realize the revocation of a single smart meter user by publishing a secret random value bound to the smart meter identity. The proposed scheme not only protects the security of smart meter electricity consumption data by using encryption, but also resists identity attacks from both internal and external adversaries by using hash functions and timestamps. Experiment shows that the proposed scheme has lower computation overhead and communication overhead than other authentication schemes and is more suitable for smart meter authentication.

A certificateless signcryption with proxy-encryption for securing agricultural data in the cloud

Precision agriculture (PA) involves collecting, processing, and analyzing datasets in agriculture for an informed decision. Due to the high data storage and application maintenance costs, farmers usually outsource their agricultural data obtained from PA to cloud service providers to leverage cloud services. Nonetheless, serious security concerns arise from using cloud services for farmers. For instance, an attacker can intercept agricultural data and run comprehensive statistical analyses to adjudicate farmers’ financial status, extort money, commit identity theft, etc. As a result, compelling data security schemes have become crucial for secure precision farming, where only legitimate users are required to access the agricultural data outsourced to the cloud. This article presents a certificateless signcryption scheme with proxy re-encryption (CLS-PRE) for secure access control in PA. An in-depth security analysis proves that the CLS-PRE scheme is secure in the Random Oracle Model. Detailed performance evaluation also shows that the scheme can reduce the time required to signcrypt and unsigncrypt messages and lower communication overhead.

A Lightweight, Efficient, and Physically Secure Key Agreement Authentication Protocol for Vehicular Networks

In the contemporary era, Vehicular Ad Hoc Networks (VANETs) have emerged as a vital technology in intelligent transportation systems, substantially enhancing the overall travel experience by providing advanced services to vehicles while ensuring driver safety. Despite the notable improvements, the inherent complexity of VANETs presents persistent security challenges, encompassing issues such as privacy preservation for vehicles, message authentication, and constraints in computational power and network bandwidth. Various authentication protocols have been designed for VANETs. However, many of these protocols exhibit significant vulnerabilities, rendering them insecure and unreliable in the face of diverse security threats, such as denial of service, replay, forgery, and impersonation attacks. Moreover, some existing schemes encounter limitations, including high computational complexity and the introduction of additional communication overhead and computational costs. To tackle these concerns, we designed a lightweight and secure identity authentication protocol based on elliptic curve cryptography with the objective of furnishing an effective and secure data transmission mechanism across a public communication channel for the Internet of Vehicles. In addition, we introduce Physically Unclonable Functions (PUFs) to ensure physical layer security during the communication process. A detailed security analysis demonstrates that the proposed protocol is resilient against various attacks. Through a comparative analysis with existing relevant protocols, in scenarios with a high density of vehicles, the algorithm demonstrates significantly lower computational costs and communication overhead than the related protocols, indicating that the proposed protocol is lightweight and efficient. Consequently, the empirical findings indicate that our protocol surpasses others in terms of reliability, user convenience, and practicality for ensuring secure data transmission within VANETs.

Secure data aggregation using quantum key management in IoT networks

The Internet of Things and Quantum Computing raise concerns, as Quantum IoT defines security that exploits quantum security management in IoT. The security of IoT is a significant concern for ensuring secure communications that must be appropriately protected to address key distribution challenges and ensure high security during data transmission. Therefore, in the critical context of IoT environments, secure data aggregation can provide access privileges for accessing network services. "Most data aggregation schemes achieve high computational efficiency; however, the cryptography mechanism faces challenges in finding a solution for the expected security desecration, especially with the advent of quantum computers utilizing public-key cryptosystems despite these limitations. In this paper, the Secure Data Aggregation using Quantum Key Management scheme, named SDA-QKM, employs public-key encryption to enhance the security level of data aggregation. The proposed system introduces traceability and stability checks for the keys to detect adversaries during the data aggregation process, providing efficient security and reducing authentication costs. Here the performance has been evaluated by comparing it with existing competing schemes in terms of data aggregation. The results demonstrate that SDA-QKM offers a robust security analysis against various threats, protecting privacy, authentication, and computation efficiency at a lower computational cost and communication overhead than existing systems.

Enabling privacy-preserving medication analysis in distributed EHR systems

The digitalization of the medical field has resulted in a vast amount of medical data stored in Electronic Health Record (EHR) systems. Among the various valuable applications of EHRs, medication efficacy analysis stands out as a powerful tool for drug development and enhancing patient treatment. This analysis relies on multi-dimensional medical data, encompassing various factors. Currently, medical research is increasingly inclined towards inter-institutional sharing of medical data to enhance the accuracy and reliability of analysis results. However, due to the sensitivity of personal medical data, privacy and security considerations must be taken into account when conducting medical analyses across institutions. In this paper, we present a novel privacy-preserving scheme for distributed medication efficacy analysis. Our work allows for the analysis of multi-dimensional medical data by leveraging the private set intersection protocol and the Paillier cryptosystem. To validate the feasibility of our scheme, we implement it using some real medical datasets. Experimental results demonstrate that our approach offers enhanced security and lower computational and communication overhead compared to the original approach, which solely processes single-dimensional data at a time.

Data aggregation algorithm for wireless sensor networks with different initial energy of nodes.

Data aggregation plays a critical role in sensor networks for efficient data collection. However, the assumption of uniform initial energy levels among sensors in existing algorithms is unrealistic in practical production applications. This discrepancy in initial energy levels significantly impacts data aggregation in sensor networks. To address this issue, we propose Data Aggregation with Different Initial Energy (DADIE), a novel algorithm that aims to enhance energy-saving, privacy-preserving efficiency, and reduce node death rates in sensor networks with varying initial energy nodes. DADIE considers the transmission distance between nodes and their initial energy levels when forming the network topology, while also limiting the number of child nodes. Furthermore, DADIE reconstructs the aggregation tree before each round of data transmission. This allows nodes closer to the receiving end with higher initial energy to undertake more data aggregation and transmission tasks while limiting energy consumption. As a result, DADIE effectively reduces the node death rate and improves the efficiency of data transmission throughout the network. To enhance network security, DADIE establishes secure transmission channels between transmission nodes prior to data transmission, and it employs slice-and-mix technology within the network. Our experimental simulations demonstrate that the proposed DADIE algorithm effectively resolves the data aggregation challenges in sensor networks with varying initial energy nodes. It achieves 5-20% lower communication overhead and energy consumption, 10-20% higher security, and 10-30% lower node mortality than existing algorithms.

Increasing Efficiency and Reliability in Multicast Routing based V2V Communication for Direction-Aware Cooperative Collision Avoidance

Mobile ad hoc networks (MANETs), which are a promising method for the intelligent transportation system, include vehicular ad hoc networks (VANETs) (ITS). Developing reliable and strong cooperative collision avoidance (CCA) strategy to mitigate the growing number of road fatalities each year is one of the main difficulties facing vehicular ad hoc networks (VANETs).A proper and successful routing method aids in the successful expansion of vehicular ad hoc networks. This study explains the architecture, interface layers, safety features, and implementation of a novel priority-based direction-aware collision avoidance system (P-DVCA). It distinguishes our study in the collision area of VANETs by accounting for realistic bi-directional traffic. The scheme begins with the development of dynamic clusters, which is difficult because of the bi-directional diverse traffic and the need to avoid collisions within and between clusters. The target node is sent an early warning message that includes the safe speed and the likelihood of a collision in order to notify it of an impending danger. To determine the least expensive, shortest one with the fewest hops between the source and the endpoint. A crucial problem with VANETs is the transmission of data from a source node to the base station. Cross-layer issues must be solved for a robust and stable collision avoidance programme to function properly in a VANET communication architecture. The results of the simulation show that the suggested scheme significantly outperforms CCM and C-RACCA in terms of cluster stability, fewer collisions, low latency, and low communication overhead. According to the findings, P-DVCA offers stable clustering, minimises network congestion, and lowers communication overhead and latency.