Authenticated Digital Currency Redaction with Stronger Privacy and Usability

With the promotion of digital currency,how to effectively solve the authenticity,privacy and usability of digital currency issuance has been a key problem.Redactable signature scheme(RSS)can provide the verification of the integrity and source of the generated sub-documents and solve the privacy problem in digital currency by removing blocks from the signed documents.Unfortunately,it has not realized the consolidation of signed documents,which can not solve the problem of merging two digital currencies.Now,we introduce the concept of weight based on the threshold secret sharing scheme(TSSS)and present a redactable signature scheme with merge algorithm(RSS-MA)using the quasi-commutative accumulator.Our scheme can reduce the communication overhead by utilizing the merge algorithm when transmitting multiple digital currency signatures.Furthermore,this can effectively hide the scale of users’private monetary assets and the number of transactions between users.While meeting the three properties of digital currency issuance,in order to ensure the availability of digital currency after redacting,editors shall not remove the relevant identification information block form digital currency.Finally,our security proof and the analysis of efficiency show that RSS-MA greatly improves the communication and computation efficiency when transmitting multiple signatures.

VKFQ:A Verifiable Keyword Frequency Query Framework with Local Differential Privacy in Blockchain

With its untameable and traceable properties,blockchain technology has been widely used in the field of data sharing.How to preserve individual privacy while enabling efficient data queries is one of the primary issues with secure data sharing.In this paper,we study verifiable keyword frequency(KF)queries with local differential privacy in blockchain.Both the numerical and the keyword attributes are present in data objects;the latter are sensitive and require privacy protection.However,prior studies in blockchain have the problem of trilemma in privacy protection and are unable to handle KF queries.We propose an efficient framework that protects data owners’privacy on keyword attributes while enabling quick and verifiable query processing for KF queries.The framework computes an estimate of a keyword’s frequency and is efficient in query time and verification object(VO)size.A utility-optimized local differential privacy technique is used for privacy protection.The data owner adds noise locally into data based on local differential privacy so that the attacker cannot infer the owner of the keywords while keeping the difference in the probability distribution of the KF within the privacy budget.We propose the VB-cm tree as the authenticated data structure(ADS).The VB-cm tree combines the Verkle tree and the Count-Min sketch(CM-sketch)to lower the VO size and query time.The VB-cm tree uses the vector commitment to verify the query results.The fixed-size CM-sketch,which summarizes the frequency of multiple keywords,is used to estimate the KF via hashing operations.We conduct an extensive evaluation of the proposed framework.The experimental results show that compared to theMerkle B+tree,the query time is reduced by 52.38%,and the VO size is reduced by more than one order of magnitude.

Enhancing Security and Privacy in Distributed Face Recognition Systems through Blockchain and GAN Technologies

The use of privacy-enhanced facial recognition has increased in response to growing concerns about data securityand privacy in the digital age. This trend is spurred by rising demand for face recognition technology in a varietyof industries, including access control, law enforcement, surveillance, and internet communication. However,the growing usage of face recognition technology has created serious concerns about data monitoring and userprivacy preferences, especially in context-aware systems. In response to these problems, this study provides a novelframework that integrates sophisticated approaches such as Generative Adversarial Networks (GANs), Blockchain,and distributed computing to solve privacy concerns while maintaining exact face recognition. The framework’spainstaking design and execution strive to strike a compromise between precise face recognition and protectingpersonal data integrity in an increasingly interconnected environment. Using cutting-edge tools like Dlib for faceanalysis,Ray Cluster for distributed computing, and Blockchain for decentralized identity verification, the proposedsystem provides scalable and secure facial analysis while protecting user privacy. The study’s contributions includethe creation of a sustainable and scalable solution for privacy-aware face recognition, the implementation of flexibleprivacy computing approaches based on Blockchain networks, and the demonstration of higher performanceover previous methods. Specifically, the proposed StyleGAN model has an outstanding accuracy rate of 93.84%while processing high-resolution images from the CelebA-HQ dataset, beating other evaluated models such asProgressive GAN 90.27%, CycleGAN 89.80%, and MGAN 80.80%. With improvements in accuracy, speed, andprivacy protection, the framework has great promise for practical use in a variety of fields that need face recognitiontechnology. This study paves the way for future research in privacy-enhanced face recognition systems, emphasizingthe significance of using cutting-edge technology to meet rising privacy issues in digital identity.

Achieving dynamic privacy measurement and protection based on reinforcement learning for mobile edge crowdsensing of IoT

With the maturity and development of 5G field,Mobile Edge CrowdSensing(MECS),as an intelligent data collection paradigm,provides a broad prospect for various applications in IoT.However,sensing users as data uploaders lack a balance between data benefits and privacy threats,leading to conservative data uploads and low revenue or excessive uploads and privacy breaches.To solve this problem,a Dynamic Privacy Measurement and Protection(DPMP)framework is proposed based on differential privacy and reinforcement learning.Firstly,a DPM model is designed to quantify the amount of data privacy,and a calculation method for personalized privacy threshold of different users is also designed.Furthermore,a Dynamic Private sensing data Selection(DPS)algorithm is proposed to help sensing users maximize data benefits within their privacy thresholds.Finally,theoretical analysis and ample experiment results show that DPMP framework is effective and efficient to achieve a balance between data benefits and sensing user privacy protection,in particular,the proposed DPMP framework has 63%and 23%higher training efficiency and data benefits,respectively,compared to the Monte Carlo algorithm.

Redundant Data Detection and Deletion to Meet Privacy Protection Requirements in Blockchain-Based Edge Computing Environment

With the rapid development of information technology,IoT devices play a huge role in physiological health data detection.The exponential growth of medical data requires us to reasonably allocate storage space for cloud servers and edge nodes.The storage capacity of edge nodes close to users is limited.We should store hotspot data in edge nodes as much as possible,so as to ensure response timeliness and access hit rate;However,the current scheme cannot guarantee that every sub-message in a complete data stored by the edge node meets the requirements of hot data;How to complete the detection and deletion of redundant data in edge nodes under the premise of protecting user privacy and data dynamic integrity has become a challenging problem.Our paper proposes a redundant data detection method that meets the privacy protection requirements.By scanning the cipher text,it is determined whether each sub-message of the data in the edge node meets the requirements of the hot data.It has the same effect as zero-knowledge proof,and it will not reveal the privacy of users.In addition,for redundant sub-data that does not meet the requirements of hot data,our paper proposes a redundant data deletion scheme that meets the dynamic integrity of the data.We use Content Extraction Signature(CES)to generate the remaining hot data signature after the redundant data is deleted.The feasibility of the scheme is proved through safety analysis and efficiency analysis.

Multi-Source Data Privacy Protection Method Based on Homomorphic Encryption and Blockchain

Multi-Source data plays an important role in the evolution of media convergence.Its fusion processing enables the further mining of data and utilization of data value and broadens the path for the sharing and dissemination of media data.However,it also faces serious problems in terms of protecting user and data privacy.Many privacy protectionmethods have been proposed to solve the problemof privacy leakage during the process of data sharing,but they suffer fromtwo flaws:1)the lack of algorithmic frameworks for specific scenarios such as dynamic datasets in the media domain;2)the inability to solve the problem of the high computational complexity of ciphertext in multi-source data privacy protection,resulting in long encryption and decryption times.In this paper,we propose a multi-source data privacy protection method based on homomorphic encryption and blockchain technology,which solves the privacy protection problem ofmulti-source heterogeneous data in the dissemination ofmedia and reduces ciphertext processing time.We deployed the proposedmethod on theHyperledger platformfor testing and compared it with the privacy protection schemes based on k-anonymity and differential privacy.The experimental results showthat the key generation,encryption,and decryption times of the proposedmethod are lower than those in data privacy protection methods based on k-anonymity technology and differential privacy technology.This significantly reduces the processing time ofmulti-source data,which gives it potential for use in many applications.

Privacy‐preserving remote sensing images recognition based on limited visual cryptography

With the arrival of new data acquisition platforms derived from the Internet of Things(IoT),this paper goes beyond the understanding of traditional remote sensing technologies.Deep fusion of remote sensing and computer vision has hit the industrial world and makes it possible to apply Artificial intelligence to solve problems such as automatic extraction of information and image interpretation.However,due to the complex architecture of IoT and the lack of a unified security protection mechanism,devices in remote sensing are vulnerable to privacy leaks when sharing data.It is necessary to design a security scheme suitable for computation‐limited devices in IoT,since traditional encryption methods are based on computational complexity.Visual Cryptography(VC)is a threshold scheme for images that can be decoded directly by the human visual system when superimposing encrypted images.The stacking‐to‐see feature and simple Boolean decryption operation make VC an ideal solution for privacy‐preserving recognition for large‐scale remote sensing images in IoT.In this study,the secure and efficient transmission of high‐resolution remote sensing images by meaningful VC is achieved.By diffusing the error between the encryption block and the original block to adjacent blocks,the degradation of quality in recovery images is mitigated.By fine‐tuning the pre‐trained model from large‐scale datasets,we improve the recognition performance of small encryption datasets for remote sensing images.The experimental results show that the proposed lightweight privacy‐preserving recognition framework maintains high recognition performance while enhancing security.

GrCol-PPFL:User-Based Group Collaborative Federated Learning Privacy Protection Framework

With the increasing number of smart devices and the development of machine learning technology,the value of users’personal data is becoming more and more important.Based on the premise of protecting users’personal privacy data,federated learning(FL)uses data stored on edge devices to realize training tasks by contributing training model parameters without revealing the original data.However,since FL can still leak the user’s original data by exchanging gradient information.The existing privacy protection strategy will increase the uplink time due to encryption measures.It is a huge challenge in terms of communication.When there are a large number of devices,the privacy protection cost of the system is higher.Based on these issues,we propose a privacy-preserving scheme of user-based group collaborative federated learning(GrCol-PPFL).Our scheme primarily divides participants into several groups and each group communicates in a chained transmission mechanism.All groups work in parallel at the same time.The server distributes a random parameter with the same dimension as the model parameter for each participant as a mask for the model parameter.We use the public datasets of modified national institute of standards and technology database(MNIST)to test the model accuracy.The experimental results show that GrCol-PPFL not only ensures the accuracy of themodel,but also ensures the security of the user’s original data when users collude with each other.Finally,through numerical experiments,we show that by changing the number of groups,we can find the optimal number of groups that reduces the uplink consumption time.

A Certificateless Homomorphic Encryption Scheme for Protecting Transaction Data Privacy of Post-Quantum Blockchain

Blockchain has a profound impact on all areas of society by virtue of its immutability,decentralization and other characteristics.However,blockchain faces the problem of data privacy leakage during the application process,and the rapid development of quantum computing also brings the threat of quantum attack to blockchain.In this paper,we propose a lattice-based certificateless fully homomorphic encryption(LCFHE)algorithm based on approximate eigenvector firstly.And we use the lattice-based delegate algorithm and preimage sampling algorithm to extract part of the private key based on certificateless scheme,which is composed of the private key together with the secret value selected by the user,thus effectively avoiding the problems of certificate management and key escrow.Secondly,we propose a post-quantum blockchain transaction privacy protection scheme based on LCFHE algorithm,which uses the ciphertext calculation characteristic of homomorphic encryption to encrypt the account balance and transaction amount,effectively protecting the transaction privacy of users and having the ability to resist quantum attacks.Finally,we analyze the correctness and security of LCFHE algorithm,and the security of the algorithm reduces to the hardness of learning with errors(LWE)hypothesis.

Residential Energy Consumption Forecasting Based on Federated Reinforcement Learning with Data Privacy Protection

Most studies have conducted experiments on predicting energy consumption by integrating data formodel training.However, the process of centralizing data can cause problems of data leakage.Meanwhile,many laws and regulationson data security and privacy have been enacted, making it difficult to centralize data, which can lead to a datasilo problem. Thus, to train the model while maintaining user privacy, we adopt a federated learning framework.However, in all classical federated learning frameworks secure aggregation, the Federated Averaging (FedAvg)method is used to directly weight the model parameters on average, which may have an adverse effect on te model.Therefore, we propose the Federated Reinforcement Learning (FedRL) model, which consists of multiple userscollaboratively training the model. Each household trains a local model on local data. These local data neverleave the local area, and only the encrypted parameters are uploaded to the central server to participate in thesecure aggregation of the global model. We improve FedAvg by incorporating a Q-learning algorithm to assignweights to each locally uploaded local model. And the model has improved predictive performance. We validatethe performance of the FedRL model by testing it on a real-world dataset and compare the experimental results withother models. The performance of our proposed method in most of the evaluation metrics is improved comparedto both the centralized and distributed models.

Secure Blockchain-Enabled Internet of Vehicles Scheme with Privacy Protection

The car-hailing platform based on Internet of Vehicles(IoV)tech-nology greatly facilitates passengers’daily car-hailing,enabling drivers to obtain orders more efficiently and obtain more significant benefits.However,to match the driver closest to the passenger,it is often necessary to process the location information of the passenger and driver,which poses a considerable threat to privacy disclosure to the passenger and driver.Targeting these issues,in this paper,by combining blockchain and Paillier homomorphic encryption algorithm,we design a secure blockchain-enabled IoV scheme with privacy protection for online car-hailing.In this scheme,firstly,we propose an encryp-tion scheme based on the lattice.Thus,the location information of passengers and drivers is encrypted in this system.Secondly,by introducing Paillier homomorphic encryption algorithm,the location matching of passengers and drivers is carried out in the ciphertext state to protect their location privacy.At last,blockchain technology is used to record the transactions in online car-hailing,which can provide a security guarantee for passengers and drivers.And we further analyze the security and performance of this scheme.Compared with other schemes,the experimental results show that the proposed scheme can protect the user’s location privacy and have a better performance.

Blockchain Privacy Protection Based on Post Quantum Threshold Algorithm

With the rapid increase in demand for data trustworthiness and data security,distributed data storage technology represented by blockchain has received unprecedented attention.These technologies have been suggested for various uses because of their remarkable ability to offer decentralization,high autonomy,full process traceability,and tamper resistance.Blockchain enables the exchange of information and value in an untrusted environment.There has been a significant increase in attention to the confidentiality and privacy preservation of blockchain technology.Ensuring data privacy is a critical concern in cryptography,and one of the most important protocols used to achieve this is the secret-sharing method.By dividing the secret into shares and distributing them among multiple parties,no one can access the secret without the cooperation of the other parties.However,Attackers with quantum computers in the future can execute Grover’s and Shor’s algorithms on quantum computers that can break or reduce the currently widely used cryptosystems.Furthermore,centralized management of keys increases the risk of key leakage.This paper proposed a post-quantum threshold algo-rithm to reduce the risk of data privacy leakage in blockchain Systems.This algorithm uses distributed key management technology to reduce the risk of individual node private key leakage and provide post-quantum security.The proposed privacy-preserving cryptographic algorithm provides a post-quantum threshold architecture for managing data,which involves defining users and interaction processes within the system.This paper applies a linear secret-sharing solution to partition the private key of the Number Theory Research Unit(NTRU)algorithm into n parts.It constructs a t–n threshold that allows recovery of the plaintext only when more than t nodes participate in decryption.The characteristic of a threshold makes the scheme resistant to collusion attacks from members whose combined credibility is less than the threshold.This mitigates the risk of single-point private key leakage.During the threshold decryption process,the private key information of the nodes will not be leaked.In addition,the fact that the threshold algorithm is founded on the NTRU lattice enables it to withstand quantum attacks,thus enhancing its security.According to the analysis,the proposed scheme provides superior protection compared to currently availablemethods.This paper provides postquantum security solutions for data security protection of blockchain,which will enrich the use of blockchain in scenarios with strict requirements for data privacy protection.

Multi-Robot Privacy-Preserving Algorithms Based on Federated Learning:A Review

The robotics industry has seen rapid development in recent years due to the Corona Virus Disease 2019.With the development of sensors and smart devices,factories and enterprises have accumulated a large amount of data in their daily production,which creates extremely favorable conditions for robots to perform machine learning.However,in recent years,people’s awareness of data privacy has been increasing,leading to the inability to circulate data between different enterprises,resulting in the emergence of data silos.The emergence of federated learning provides a feasible solution to this problem,and the combination of federated learning and multi-robot systems can break down data silos and improve the overall performance of robots.However,as scholars have studied more deeply,they found that federated learning has very limited privacy protection.Therefore,how to protect data privacy from infringement remains an important issue.In this paper,we first give a brief introduction to the current development of multi-robot and federated learning;second,we review three aspects of privacy protection methods commonly used,privacy protection methods for multi-robot,and Other Problems Faced by Multi-robot Systems,focusing on method comparisons and challenges;and finally draw conclusions and predict possible future research directions.

Ensuring User Privacy and Model Security via Machine Unlearning: A Review

As an emerging discipline,machine learning has been widely used in artificial intelligence,education,meteorology and other fields.In the training of machine learning models,trainers need to use a large amount of practical data,which inevitably involves user privacy.Besides,by polluting the training data,a malicious adversary can poison the model,thus compromising model security.The data provider hopes that the model trainer can prove to them the confidentiality of the model.Trainer will be required to withdraw data when the trust collapses.In the meantime,trainers hope to forget the injected data to regain security when finding crafted poisoned data after the model training.Therefore,we focus on forgetting systems,the process of which we call machine unlearning,capable of forgetting specific data entirely and efficiently.In this paper,we present the first comprehensive survey of this realm.We summarize and categorize existing machine unlearning methods based on their characteristics and analyze the relation between machine unlearning and relevant fields(e.g.,inference attacks and data poisoning attacks).Finally,we briefly conclude the existing research directions.

A Dynamic Multi-Attribute Resource Bidding Mechanism with Privacy Protection in Edge Computing

In edge computing,a reasonable edge resource bidding mechanism can enable edge providers and users to obtain benefits in a relatively fair fashion.To maximize such benefits,this paper proposes a dynamic multiattribute resource bidding mechanism(DMRBM).Most of the previous work mainly relies on a third-party agent to exchange information to gain optimal benefits.It isworth noting thatwhen edge providers and users trade with thirdparty agents which are not entirely reliable and trustworthy,their sensitive information is prone to be leaked.Moreover,the privacy protection of edge providers and users must be considered in the dynamic pricing/transaction process,which is also very challenging.Therefore,this paper first adopts a privacy protection algorithm to prevent sensitive information from leakage.On the premise that the sensitive data of both edge providers and users are protected,the prices of providers fluctuate within a certain range.Then,users can choose appropriate edge providers by the price-performance ratio(PPR)standard and the reward of lower price(LPR)standard according to their demands.The two standards can be evolved by two evaluation functions.Furthermore,this paper employs an approximate computing method to get an approximate solution of DMRBM in polynomial time.Specifically,this paper models the bidding process as a non-cooperative game and obtains the approximate optimal solution based on two standards according to the game theory.Through the extensive experiments,this paper demonstrates that the DMRBM satisfies the individual rationality,budget balance,and privacy protection and it can also increase the task offloading rate and the system benefits.

A Conditionally Anonymous Linkable Ring Signature for Blockchain Privacy Protection

In recent years,the issue of preserving the privacy of parties involved in blockchain transactions has garnered significant attention.To ensure privacy protection for both sides of the transaction,many researchers are using ring signature technology instead of the original signature technology.However,in practice,identifying the signer of an illegal blockchain transaction once it has been placed on the chain necessitates a signature technique that offers conditional anonymity.Some illegals can conduct illegal transactions and evade the lawusing ring signatures,which offer perfect anonymity.This paper firstly constructs a conditionally anonymous linkable ring signature using the Diffie-Hellman key exchange protocol and the Elliptic Curve Discrete Logarithm,which offers a non-interactive process for finding the signer of a ring signature in a specific case.Secondly,this paper’s proposed scheme is proven correct and secure under Elliptic Curve Discrete Logarithm Assumptions.Lastly,compared to previous constructions,the scheme presented in this paper provides a non-interactive,efficient,and secure confirmation process.In addition,this paper presents the implementation of the proposed scheme on a personal computer,where the confirmation process takes only 2,16,and 24ms for ring sizes of 4,24 and 48,respectively,and the confirmation process can be combined with a smart contract on the blockchain with a tested millisecond level of running efficiency.In conclusion,the proposed scheme offers a solution to the challenge of identifying the signer of an illegal blockchain transaction,making it an essential contribution to the field.

FedNRM:A Federal Personalized News Recommendation Model Achieving User Privacy Protection

In recent years,the type and quantity of news are growing rapidly,and it is not easy for users to find the news they are interested in the massive amount of news.A news recommendation system can score and predict the candidate news,and finally recommend the news with high scores to users.However,existing user models usually only consider users’long-term interests and ignore users’recent interests,which affects users’usage experience.Therefore,this paper introduces gated recurrent unit(GRU)sequence network to capture users’short-term interests and combines users’short-term interests and long-terminterests to characterize users.While existing models often only use the user’s browsing history and ignore the variability of different users’interest in the same news,we introduce additional user’s ID information and apply the personalized attention mechanism for user representation.Thus,we achieve a more accurate user representation.We also consider the risk of compromising user privacy if the user model training is placed on the server side.To solve this problem,we design the training of the user model locally on the client side by introducing a federated learning framework to keep the user’s browsing history on the client side.We further employ secure multiparty computation to request news representations from the server side,which protects privacy to some extent.Extensive experiments on a real-world news dataset show that our proposed news recommendation model has a better improvement in several performance evaluation metrics.Compared with the current state-of-the-art federated news recommendation models,our model has increased by 0.54%in AUC,1.97%in MRR,2.59%in nDCG@5%,and 1.89%in nDCG@10.At the same time,because we use a federated learning framework,compared with other centralized news recommendation methods,we achieve privacy protection for users.

Adversarial Examples Protect Your Privacy on Speech Enhancement System

Speech is easily leaked imperceptibly.When people use their phones,the personal voice assistant is constantly listening and waiting to be activated.Private content in speech may be maliciously extracted through automatic speech recognition(ASR)technology by some applications on phone devices.To guarantee that the recognized speech content is accurate,speech enhancement technology is used to denoise the input speech.Speech enhancement technology has developed rapidly along with deep neural networks(DNNs),but adversarial examples can cause DNNs to fail.Considering that the vulnerability of DNN can be used to protect the privacy in speech.In this work,we propose an adversarial method to degrade speech enhancement systems,which can prevent the malicious extraction of private information in speech.Experimental results show that the generated enhanced adversarial examples can be removed most content of the target speech or replaced with target speech content by speech enhancement.The word error rate(WER)between the enhanced original example and enhanced adversarial example recognition result can reach 89.0%.WER of target attack between enhanced adversarial example and target example is low at 33.75%.The adversarial perturbation in the adversarial example can bring much more change than itself.The rate of difference between two enhanced examples and adversarial perturbation can reach more than 1.4430.Meanwhile,the transferability between different speech enhancement models is also investigated.The low transferability of the method can be used to ensure the content in the adversarial example is not damaged,the useful information can be extracted by the friendly ASR.This work can prevent the malicious extraction of speech.

Research on Residents’Willingness to Protect Privacy in the Context of the Personal Information Protection Law:A Survey Based on Foshan Residents’Data

The Personal Information Protection Law,as the first law on personal information protection in China,hits the people’s most concerned,realistic and direct privacy and information security issues,and plays an extremely important role in promoting the development of the digital economy,the legalization of socialism with Chinese characteristics and social public security,and marks a new historical development stage in the protection of personal information in China.However,the awareness of privacy protection and privacy protection behavior of the public in personal information privacy protection is weak.Based on the literature review and in-depth understanding of current legal regulations,this study integrates the relevant literature and theoretical knowledge of the Personal Protection Law to construct a conceptual model of“privacy information protection willingness-privacy information protection behavior”.Taking the residents of Foshan City as an example,this paper conducts a questionnaire survey on their attitudes toward the Personal Protection Law,analyzes the factors influencing their willingness to protect their privacy and their behaviors,and explores the mechanisms of their influencing variables,to provide advice and suggestions for promoting the protection of privacy information and building a security barrier for the high-quality development of public information security.

Cybersecurity and Privacy Protection in Vehicular Networks (VANETs)

As Vehicular ad hoc networks (VANETs) become more sophisticated, the importance of integrating data protection and cybersecurity is increasingly evident. This paper offers a comprehensive investigation into the challenges and solutions associated with the privacy implications within VANETs, rooted in an intricate landscape of cross-jurisdictional data protection regulations. Our examination underscores the unique nature of VANETs, which, unlike other ad-hoc networks, demand heightened security and privacy considerations due to their exposure to sensitive data such as vehicle identifiers, routes, and more. Through a rigorous exploration of pseudonymization schemes, with a notable emphasis on the Density-based Location Privacy (DLP) method, we elucidate the potential to mitigate and sometimes sidestep the heavy compliance burdens associated with data protection laws. Furthermore, this paper illuminates the cybersecurity vulnerabilities inherent to VANETs, proposing robust countermeasures, including secure data transmission protocols. In synthesizing our findings, we advocate for the proactive adoption of protective mechanisms to facilitate the broader acceptance of VANET technology while concurrently addressing regulatory and cybersecurity hurdles.