Lightweight Privacy Research Articles

A Blockchain-assisted lightweight privacy preserving authentication protocol for peer-to-peer communication in vehicular ad-hoc network

A Blockchain-assisted lightweight privacy preserving authentication protocol for peer-to-peer communication in vehicular ad-hoc network

Smart industrial information integration: a lightweight privacy protection model in an intelligent manufacturing architecture

In the industrial ecosystem, both the manufacturing and edge clouds collaborate via virtualisation technology, playing a crucial role in the efficient aggregation and processing of vast data from endpoints, offering computational and analytical support for enhanced decision-making in all industrial operations. Consequently, ensuring the reliability of virtualised manufacturing environments is paramount. Remote attestation offers a viable solution, however, current schemes are plagued by inefficiencies and privacy leakage issues. To address these concerns, we propose an efficient remote attestation approach. The approach actively conceals sensitive industrial information in measurement logs by using a hash algorithm, thereby preventing privacy breaches. Furthermore, it streamlines log entries with a log compression algorithm, enhancing remote verification efficiency in the industrial information architecture. Extensive experiments were conducted in an authentic cloud environment by applying this approach across both mesh and hierarchical remote verification structures, reducing time overhead by approximately 81.32%. Additionally, it marginally impacts server performance with only a 1.49% reduction. The results affirm the proposed approach's exceptional efficacy in decreasing verification durations and mitigating remote verification attacks, demonstrating its promising utility and feasibility in the intelligent industrial platform.

Lightweight Privacy Preserving Scheme for IoT based Smart Home

Background: The Internet of Things (IoT) is the interconnection of physical devices, controllers, sensors and actuators that monitor and share data to another end. In a smart home network, users can remotely access and control home appliances/devices via wireless channels. Due to the increasing demand for smart IoT devices, secure communication also becomes the biggest challenge. Hence, a lightweight authentication scheme is required to secure these devices and maintain user privacy. The protocol proposed is secure against different kinds of attacks and as well as is efficient. Methods: The proposed protocol offers mutual authentication using shared session key establishment. The shared session key is established between the smart device and the home gateway, ensuring that the communication between the smart devices, home gateway, and the user is secure and no third party can access the information shared. Results: Informal and formal analysis of the proposed scheme is done using the AVISPA tool. Finally, the results of the proposed scheme also compare with existing security schemes in terms of computation and communication performance cost. The results show that the proposed scheme is more efficient and robust against different types of attacks than the existing protocols. Conclusion: In the upcoming years, there will be a dedicated network system built inside the home so that the user can have access to the home from anywhere. The proposed scheme offers secure communication between the user, the smart home, and different smart devices. The proposed protocol makes sure that security and privacy are maintained since the smart devices lack computation power which makes them vulnerable to different attacks.

Comments on “Lightweight Privacy and Security Computing for Blockchained Federated Learning in IoT”

Comments on “Lightweight Privacy and Security Computing for Blockchained Federated Learning in IoT”

Lightweight Privacy Protection via Adversarial Sample

Adversarial sample-based privacy protection has its own advantages compared to traditional privacy protections. Previous adversarial sample privacy protections have mostly been centralized or have not considered the issue of hardware device limitations when conducting privacy protection, especially on the user’s local device. This work attempts to reduce the requirements of adversarial sample privacy protections on devices, making the privacy protection more locally friendly. Adversarial sample-based privacy protections rely on deep learning models, which generally have a large number of parameters, posing challenges for deployment. Fortunately, the model structural pruning technique has been proposed, which can be employed to reduce the parameter count of deep learning models. Based on the model pruning technique Depgraph and existing adversarial sample privacy protections AttriGuard and MemGuard, we design two structural pruning-based adversarial sample privacy protections, in which the user obtains the perturbed data through the pruned deep learning model. Extensive experiments are conducted on four datasets, and the results demonstrate the effectiveness of our adversarial sample privacy protection based on structural pruning.

A lightweight privacy preserving distributed certificate-less aggregate based mutual authentication scheme for vehicular adhoc networks

A lightweight privacy preserving distributed certificate-less aggregate based mutual authentication scheme for vehicular adhoc networks

LR-SDP: lightweight privacy preservation approach for distributed mobile devices in IoT environment

LR-SDP: lightweight privacy preservation approach for distributed mobile devices in IoT environment

LR-SDP: Lightweight Privacy Preservation Approach for Distributed Mobile Devices in IoT Environment

LR-SDP: Lightweight Privacy Preservation Approach for Distributed Mobile Devices in IoT Environment

A Lightweight Privacy Preservation Protocol for IOT

A Lightweight Privacy Preservation Protocol for IOT

An efficient federated learning based intrusion detection system using LS2DNN with PBKA based lightweight privacy preservation in cloud server

An efficient federated learning based intrusion detection system using LS2DNN with PBKA based lightweight privacy preservation in cloud server

Lightweight Privacy and Security Computing for Blockchained Federated Learning in IoT

The development of Internet of Things (IoT) makes human life more intelligent, and the interconnection of all things has become a reality. However, the surge in the number of devices and centralized management brings severe challenges to IoT, such as single point of failure, poor security, privacy leakage, and low reliability. Due to the decentralization, verifiability and privacy protection of blockchain federated learning, some blockchain federated learning schemes have been proposed to solve these problems, but bring new challenges, such as device privacy leakage and heavy security computing load. In this paper, we propose a new decentralized, secure and verifiable consortium blockchain federated learning privacy protection scheme, named LPBFL, which realizes lightweight computing while ensuring the privacy of the local model and dataset of the device. To achieve lightweight privacy protection, LPBFL adopts the Paillier encryption and the newly designed lightweight digital signature and batch verification algorithm. Additionally, considering that devices upload invalid or even toxic local models intentionally or unintentionally, we design a device reputation selection mechanism to make blockchain federated learning more efficient. Finally, the theoretical analysis proves the security of LPBFL and verifies the unforgeability of the proposed digital signature. Comprehensive comparisons and extensive experiments demonstrate that our LPBFL has significant advantages in multiple aspects.

Bayesian-Based Inference Attack Method and Individual Differential Privacy-Based Auction Mechanism for Double Auction Market

Due to high convenience and efficiency, electronic auction technology has been developed rapidly and has been applied to many online trading market applications. As more attention has been paid to information security, the privacy issues in the electronic auction have been widely studied. Differential privacy, as a lightweight privacy protection method, is an important direction in privacy preserving auction mechanism designing. However, most of the existing researches on differential privacy-based auction mechanism have not proposed a theoretical privacy inference attack method against the auction market. Therefore, the existence of privacy attacks is questionable, and the necessity and privacy protection performance of the existing differential privacy auction mechanism cannot be verified. To this end, in this paper we addressed the privacy attack issue and privacy protection issue in the auction market simultaneously. First, a Bayesian-based inference attack method against the double auction market was proposed from the perspective of the adversary. Theoretical analysis and evaluation results showed that the proposed inference attack method can effectively infer the bidding information of the target bidders, and attack success rate achieved approximately 95%. Second, an individual differential privacy-based auction mechanism was proposed from the perspective of the auction platform. Since not all the bidders will be attacked, we introduced the concept of individual differential privacy to provide targeted defense for specific bidders. Theoretical analysis demonstrated that the proposed auction mechanism satisfies <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2\varepsilon $ </tex-math></inline-formula> -individual differential privacy. And the extensive evaluation results showed that, compared with the existing differential privacy-based auction mechanism, our proposed mechanism provided the best privacy protection performance, that is, reduced the attack success rate to 20%, and ensured better auction performance, such as social welfare and satisfaction ratio, than the other mechanisms. Note to Practitioners—In this paper, we addressed the non-invasive privacy issues in the widely used electronic auction mechanism. Most of the previous works focused on designing differential privacy based auction mechanism against the non-invasive privacy attack, but neglecting the principle of non-invasive privacy attack methods. This makes it impossible to verify the privacy protection effectiveness of their proposed mechanisms. For this reason, a very large privacy budget may be selected to ensure the efficiency of privacy protection, which will lead to poor auction performance. To this end, we first proposed a Bayesian-based inference attack method against the double auction market. The proposed inference attack method allows the adversary infer the bidders’ private bidding information by the public auction results. Moreover, we then proposed an individual differential privacy auction mechanism, which aimed to achieve effective privacy protection while minimizing the added noise, thereby improving auction performance. The experiments demonstrate that the proposed Bayesian-based inference attack method achieves a good attack successful rate, and the proposed individual differential privacy auction mechanism will achieve the better efficiency of privacy protection as well as auction performance comparing with the exist differential privacy-based auction mechanism. In conclusion, this paper provides a verification method for the future research on the privacy protection of electronic auction mechanism. Meanwhile, this paper proposes an efficient privacy protection auction mechanism, which can be used in various trading scenarios.

Privacy-Preserving Federated Learning for Internet of Medical Things under Edge Computing.

Edge intelligent computing is widely used in the fields, such as the Internet of Medical Things (IoMT) and industrial control UAV clusters, which has advantages, including high data processing efficiency, strong real-time performance and low network delay. However, there are many problems including privacy disclosure, limited calculation force when edge intelligent devices, edge gateways and clouds complete the task unloading, as well as scheduling and coordination issues. Federated learning allows all training devices to complete training at the same time, which greatly improves training efficiency. However, traditional federated learning will expose patient's privacy information of the training set. Due to the sensitive nature of the healthcare data, the aforementioned approach of transferring the patient's data to the central servers may create serious security and privacy issues. Therefore, this article proposes a Privacy Protection Scheme for Federated Learning under Edge Computing (PPFLEC). First of all, we propose a lightweight privacy protection protocol based on a shared secret and weight mask, which is based on a random mask scheme of secret sharing. It is more accurate and efficient than federated learning for secure multiparty computing frameworks based on homomorphic encryption. It can not only protect gradient privacy without losing model accuracy, but also resist equipment dropping and collusion attacks between devices. Second, we design an algorithm based on a digital signature and hash function, which achieves the \textcolor{blue}{integrity and consistency} of the message, as well as resisting replay attacks. Finally, we propose a periodic average training strategy, compared with differential privacy to prove that our scheme is 40% faster in efficiency than in deferential privacy. Meanwhile, compared with federated learning, we can achieve the same efficiency under the condition of ensuring safety. Therefore, our scheme can work well in unstable edge computing environments such as smart healthcare.

Improved Gentry–Halevi's fully homomorphic encryption‐based lightweight privacy preserving scheme for securing medical Internet of Things

AbstractInternet of Medical Things (IoMT) solutions have proliferated rapidly in the COVID‐19 pandemic era. The smart medical sensors capture real‐time data from remote patients and communicate it to medical servers in a secure and privacy‐preserving manner. It is a herculean challenge to guarantee security and privacy in Medical IoT applications. Hence, an improved Gentry–Halevi's fully homomorphic encryption‐based (IGHFHE) lightweight privacy preserving user authentication scheme is proposed in this work. The scheme is proposed with an integer matrix computation strategy for securing data computation with privacy protection. It adopts the translation process of Gentry–Halevi's fully homomorphic encryption process for performing homomorphic addition and multiplication, then encrypt an integer matrix modulo that represents a positive integer. Extensive informal investigation and simulation of the proposed IGHFHE scheme shows that it is more resistant to well‐known attacks for preventing authentication breaches. Also, the proposed IGHFHE scheme reduced computational and storage overhead by 4.98% and 5.78% respectively on average in comparison to other prevailing schemes.

A Trustworthy, Reliable, and Lightweight Privacy and Data Integrity Approach for the Internet of Things

Data integrity and authenticity are among the key challenges faced by the interacting devices of Internet of Things (IoT). The resource-constrained nature of sensor-embedded devices makes it even more difficult to design lightweight security schemes for these networks. In view of limited resources of the IoT devices, this article proposes a lightweight and trustworthy device-to-server mutual authentication scheme for edge-enabled IoT networks. Initially, a trusted authority generates and assigns identities (IDs) and mask them to servers and clients, also known as member devices, in an offline phase. These IDs are utilized to prevent possible infiltration of the adversary device(s). Next, every device ensures the authenticity of requesting devices using a sophisticated challenge, which is encrypted using a 128-b secret key, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\lambda _{i}$</tex-math></inline-formula> . Each device expects a reply from the intended destination device for resolving the encrypted challenge within the defined timeframe, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$i.e., \bigtriangleup T$</tex-math></inline-formula> . Moreover, authenticity of the requesting device is verified through the stored IDs, which are shared in the offline phase. Simulation results have verified the exceptional performance of the proposed authentication scheme against field proven approaches in terms of computational and communication costs.

The Security of “2FLIP” Authentication Scheme for VANETs: Attacks and Rectifications

Wireless broadcast transmission enables Inter-vehicle or Vehicle-to-Vehicle (V2V) communication among nearby vehicles and with nearby fixed equipment, referred to as Road Side Units (RSUs). The vehicles and RSUs within transmission range establish a self-organizing network called Vehicular Ad-hoc Network (VANET). The V2V communication in VANETs is vulnerable to cyber-attacks involving message manipulation. Thus, mechanisms should be applied to ensure both the authenticity and integrity of the data broadcast. However, due to privacy concerns, it is important to avoid the use of identifiers that may aid tracking and surveillance of drivers. This is a serious constraint on authentication mechanisms. Recently, Wang et al. [1] proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A Two-Factor Lightweight Privacy Preserving Authentication Scheme for VANET</i> named 2FLIP. They claim that their scheme includes a secure systemkey update protocol to restore the whole system when necessary. In this paper, we show that this is incorrect: 2FLIP does not provide perfect forward secrecy. This results in a known-key attack, as well as message forgery attack by an external adversary who may be an unregistered vehicle user. This external adversary can generate valid anonymous messages and further, they cannot be traced. The 2FLIP scheme is efficient, so we propose a modification to improve the security. We provide a formal security proof to show that our proposal is indeed provably secure. We demonstrate the efficiency of our proposal by conducting extensive performance analysis. We believe the enhanced system-key update protocol will be useful for application by researchers and designers in current and future VANET authentication schemes.

RSA Based Consensus Algorithm for Lightweight Private Blockchain Network

Consensus algorithms are essential for achieving agreement among nodes in blockchain systems. However, traditional consensus algorithms such as Proof of Work (PoW) and Proof of Stake (PoS) can be resource-intensive and unsuitable for lightweight private blockchain applications. This paper proposes using the RSA (Rivest–Shamir–Adleman) encryption algorithm as a consensus algorithm for a lightweight private blockchain in the context of a college placement system. RSA offers several advantages over traditional consensus algorithms, including simplicity, efficiency, and security. Moreover, RSA can be implemented on resource-constrained nodes, making it a promising solution for lightweight blockchain applications. The resource constrained nodes are students, academic department, training and placement cell department, and placement cell coordinator. The company acts as a client. The movement of the student’s data to companies is recorded as transactions on the distributed ledger or blockchain, allowing the student to track its progress.

The Low-light License Plate Recognition via CNN

The low-light license plate recognition (LPR) is an important task in LPR, and the task of low-light LPR is a challenge in LPR. Compared with ordinary LPR, low-light LPR is more challenging. The first is that there are few studies on low-light LPR, and there is a lack of dedicated datasets. Besides, there are few lightweight networks dedicated to low-light LPR. The lack of lightweight private networks makes it difficult to deploy LPR methods efficiently. Based on this, this paper proposes a low-light LPR method. Specifically, we propose a dataset dedicated to low-light LPR with a sample size of 19,121. Besides, we propose a lightweight LPR method implemented using a lightweight architecture similar to VGG. We also compare with some commonly used lightweight methods, and the comparison results show that our proposed method has better performance. In addition, we also visualized some data that are difficult for the model to identify, and found several types of challenging data. This result is helpful for subsequent researchers to improve on this type of data.

Light-weight privacy enabled topology establishment and communication protocol for swarm IoT networks

Light-weight privacy enabled topology establishment and communication protocol for swarm IoT networks

A lightweight privacy preserving scheme of charging and discharging for electric vehicles based on consortium blockchain in charging service company

Charging service companies (CSC) provide charging and discharging services for electric vehicles (EV) by managing numerous charging piles (CP). At that time, the privacy preservation of EV users has become a problem that must be solved. The lightweight solution has a broad applicated prospect because of its excellent efficiency and security. Simultaneously, the centralized storage of massive EVs’ real-time power information (RPI) may cause some risks, such as data tampering and single point of failure. Therefore, a lightweight privacy preserving scheme for EVs based on consortium blockchain (CB) in CSC is proposed to solve the problems mentioned above. The scheme has the following novelties and contributions: (1) a pseudo identity algorithm is designed to realize EV users to charge, discharge, and enjoy other services with pseudo identity, which can protect EV users’ privacy and security; (2) a certificateless aggregation signcryption algorithm is designed to realize lightweight RPI secure communication: in this algorithm, the function of information signature and encryption in one operation is realized, and the operation steps are simplified; bilinear pairing and exponential operation are not used to improve the efficiency of aggregate signcryption; batch aggregation and verification are designed to ensure the EV’s RPI can be stored in the CB securely and efficiently; (3) the CB technology is adopted to solve the problems of data tampering and single point of failure of the central organization. Moreover, the consensus mechanism in the CB is designed in this scheme to further improve the scheme’s efficiency. Finally, theoretical analysis proves that the scheme can guarantee users’ privacy. Experiments show that for 800 EV users, the time overhead of our scheme is only 1969.5 s, which is lower than other existing schemes.