ABSTRACT The rapid advancement of cloud computing and big data has led to an increasing number of data owners seeking to outsource encrypted data to cloud servers. Searchable encryption (SE) is recognized as a crucial cryptographic primitive for data owners to retrieve data in this context. However, most current SE schemes suffer from low keyword search accuracy and an inability to verify search results. Additionally, encrypted data should be shared with specific data users without compromising their privacy. Therefore, this article introduces a novel fuzzy keyword‐enabled ranked searchable ciphertext‐policy attribute‐based encryption (FKRSCPABE) scheme to address these challenges. First, we combine ciphertext‐policy attribute‐based encryption (CP‐ABE) and public‐key encryption with keyword search (PEKS) to realize fine‐grained access control and efficiently search outsourced encrypted data for data owners. Second, since the document almost certainly contains typographical errors, we implement fuzzy keyword search to ensure meaningful and accurate results. Additionally, we employ probabilistic trapdoors to resist distinguishability attacks. Furthermore, we rank documents using weighted regional scores to improve search accuracy. As a result, our scheme achieves IND‐CCA security by using the classic Canetti transformation. In brief, our scheme is able to achieve fuzzy keyword search and better ranking of search results, while also ensuring data security.

Mobile Crowdsensing (MCS) has emerged as a promising paradigm for large-scale, real-time data collection by leveraging the sensing capabilities of widely distributed mobile workers. However, its practical adoption is challenged by privacy risks and unsustainable incentive structures that inadequately compensate for workers' inherent participation costs, leading to diminished motivation, sparse task coverage, and reduced data availability. Existing approaches either provide limited and utility-degrading privacy protection or design incentive mechanisms that incur substantial costs, even under the Nash equilibrium. To bridge this gap, we propose C-PRISM (Compre hensive Privacy-preserving and Behavioral-Incentive Sustainable crowdsensing Mechanism), an integrated framework that seamlessly combines privacy-preserving techniques with behavioral economic incentive design. Specifically, C-PRISM employs ran domized matrix perturbation for fine-grained location protection and a two-phase proxy re-encryption protocol to secure task details and sensing data across evaluation, recruitment, and transmission. Building upon this secure foundation, behavioral economic incentives grounded in prospect theory, the Aronson effect, and a dual reference-point model are introduced to promote sustained worker participation at sub-Nash-equilibrium costs. Rigorous theoretical analysis validates C-PRISM's security and individual rationality. Extensive experiments on real-world datasets demonstrate that C-PRISM increases data collection efficiency by 7.42%-193.75%, improves worker retention by 2.53%-69.78%, and effectively maintains overall system utility.

Vehicle-to-Infrastructure (V2I) collaboration constitutes an emerging paradigm for advancing autonomous driving. However, the integrated collaboration of perception, communication, and computation within V2I system remains a critical challenge. To address it, we propose a Software-Defined Network (SDN)-based collaborative approach for Autonomous Vehicle and Road Infrastructure Network (AVRIN). The architecture designates road infrastructures as road nodes and autonomous vehicles as dynamic vehicle nodes, establishing AVRIN through SDN. The control plane dynamically maintains global network topology and distributed flow tables by continuously evaluating node accessibility, while the forwarding plane is responsible for packet transmission via the OpenFlow protocol. In the perception module, road nodes divide the perception range into spatial units, whereas vehicle nodes dynamically align these units with their drivable areas across temporal sequences. Through coordinated communication and computation modules, road nodes strategically allocate dedicated bandwidth and computational resources. Building on this approach, we develop a particle swarm-based multi-objective optimization algorithm to achieve balanced co-optimization across perception, communication, and computation. Experimental validation demonstrates its superior collaborative Bird's Eye View (BEV) detection performance on the V2X-Sim 2.0 dataset, outperforming existing approaches by 10.37% in mean Average Precision. Furthermore, evaluations on the newly collected Jiading dataset, from a real-world urban roadway, confirm the approach's robustness with 1.823-second computation time under dynamic network conditions.

Numerous mobile devices have enabled Mobile Crowdsensing (MCS) to recruit a massive number of workers to sense and collect data for data requestors, thereby supporting a wide range of data-driven services. However, due to its crowdsourcing nature, MCS services face two critical challenges: ensuring privacy preservation and maintaining high-quality data collection. To address these issues, this paper proposes a privacy preserving and data quality optimization framework, named DQO-P5PI, which integrates the Preservation of Five Privacy Information (P5PI) framework with a Data Quality Optimization (DQO) scheme based on Fuzzy sets. Specifically, the P5PI frame work focuses on protecting five types of sensitive information for workers, namely the sensing data, the Reputation, the Identity, the Bid, and the Location (collectively referred to as DRIBL). In the P5PI framework, homomorphic encryption is employed to ensure DRIBL information privacy; the pseudonym mechanism is adopted to protect worker identities during interactions; Credit Verification Methods (CVM) verify the validity of workers' reputation scores, thereby protecting reputation privacy; and a Homomorphic Encryption Comparison Method (HECM) enables the platform to assess worker eligibility and select the top-k workers with the highest comprehensive scores. The DQO scheme further incorporates a Worker Reputation Update Method (WRUM) to dynamically update workers' reputation parameters, and a Credit Rating Method (CRM) that leverages Fuzzy sets to integrate bid values with historical reputation information for more reliable task allocation. We theoretically prove the correctness of the P5PI framework and DQO scheme, and extensive experimental results demonstrate that the proposed framework effectively improves sensing quality while keeping costs low.

At present, cloud computing provides flexible and cost-effective solutions for sharing medical data, particularly electronic health records (EHRs), which are widely used by resource-limited healthcare institutions. However, the sensitive nature of patient data demands strong encryption and privacy protection. Additionally, ensuring data traceability-tracking and monitoring data sources and usage remains a crucial yet unresolved challenge. Current solutions prioritize data integrity during sharing but lack transparency, traceability, and comprehensive integrity audits for cloud-stored medical data. To overcome these limitations, this paper puts forward a blockchain-based framework for medical data sharing, enabling traceable data usage and integrating automatic audit mechanism. Our scheme adopts smart contracts on the blockchain to verify search results, audit cloud data integrity, and allocate service fees equitably based on audits. The proposed solution ensures the integrity of data during the sharing process and storage, while fostering financial fairness between users and cloud service providers. Rigorous experiments demonstrate that our scheme substantially improves verification efficiency, achieving up to a 95% reduction in smart contract gas consumption and an 80%–85% decrease in post-update verification latency compared to existing state-of-the-art methods, making it highly suitable for medical data-sharing environments characterized by frequent updates and stringent data integrity requirements.

Currently, deep neural networks (DNNs) are susceptible to adversarial attacks, particularly when the network's structure and parameters are known, while most of the existing attacks do not perform satisfactorily in the presence of black-box settings. In this context, model augmentation is considered to be effective to improve the success rates of black-box attacks on adversarial examples. However, the existing model augmentation methods tend to rely on a single transformation, which limits the diversity of augmented model collections and thus affects the transferability of adversarial examples. In this paper, we first propose the random diversity ensemble method (RDE-MI-FGSM) to effectively enhance the diversity of the augmented model collection, thereby improving the transferability of the generated adversarial examples. Afterwards, we put forward the random diversity variance ensemble method (RDE-VRA-MI-FGSM), which adopts variance reduction augmentation (VRA) to improve the gradient variance of the enhanced model set and avoid falling into a poor local optimum, so as to further improve the transferability of adversarial examples. Furthermore, experimental results demonstrate that our approaches are compatible with many existing transfer-based attacks and can effectively improve the transferability of gradient-based adversarial attacks on the ImageNet dataset. Also, our proposals have achieved higher attack success rates even if the target model adopts advanced defenses. Specifically, we have achieved an average attack success rate of 91.4% on the defense model, which is higher than other baseline approaches.

As an important cryptographic primitive, searchable encryption (SE) plays a crucial role in performing keyword searching on encrypted texts. However, in order to realize fuzzy keyword search, most fuzzy search encryption schemes utilize wildcards and gram technology to construct fuzzy sets, which consumes a lot of storage and computational resources. Therefore, in this paper, we propose a new verifiable Chinese multi-keyword fuzzy rank searchable encryption (VCMKFRSE) scheme. Firstly, we take advantage of the Yongzi Ba method to convert Chinese keywords into stroke strings, and employ chinese keywords vector generation algorithm to convert the stroke string into a keyword vector. Secondly, we utilize inverted index tables to establish the relationship between keywords and documents. In particular, the relevance score between keywords and documents is calculated by using the three-factor algorithm. Also, we adopt the MinHash function to construct a fuzzy index table for each keyword vector. Thirdly, in order to realize the authentication of search results and avoid receiving useless search results, we build an authentication tag table by using an authentication tag generation function. Afterwards, we apply probabilistic trapdoors to resist distinguishability attacks. At last, our scheme achieves IND-CCA secure and is more efficient comparing with the state of the art. Overall, our proposal achieves fuzzy multi-keyword search and more accurate search result ranking, while ensuring data security and higher efficiency.

The high mobility of Internet of Unmanned Aerial Vehicles (IUAVs) has attracted attention in the field of data collection. With the rapid development of the Internet of Things (IoT), more and more data are generated by IoT networks. IUAV-aided IoT networks can efficiently collect data in specific areas, which is of great significance in disaster relief. In the data collection task, it is necessary to plan the flight trajectory for the data collector—IUAV, so that the IUAV can collect data efficiently. However, existing research basically only considers the efficiency of data collection by IUAVs, but rarely considers the safety of IUAVs during flight. Therefore, this paper proposes an IUAV trajectory planning algorithm that integrates energy efficiency and safety using local search to address the issues mentioned above. At the same time, a Tiny Machine Learning (TinyML) algorithm is designed to assist the IUAV in making real-time decisions during flight. First, we build a general mathematical model that describes the risk in a particular region. Then consider guiding the IUAV to a safer trajectory by introducing virtual nodes in the flight trajectory. Furthermore, we designed a local search algorithm for the three tasks of IUAV access sequence, IoT Networks cluster heads selection and virtual nodes selection, and solved them through iterative optimization. We also consider the unreachable situation of the virtual nodes and use TinyML technology to help the IUAV adjust the position of the virtual nodes in real time in case of an emergency.In the end, an IUAV trajectory is obtained that can efficiently collect IoT networks’ data and fly safely. We have conducted a large number of simulation experiments to demonstrate the efficiency of the proposed algorithm compared to the baseline algorithm.

The rapid advancement and innovation of the Internet have brought about significant changes in the sharing of data through cloud storage services. Despite these advancements, cloud storage services continue to encounter challenges related to privacy protection and fine-grained access control. To address these issues, we introduce a novel secure attribute-based blockchain (BC) data access control framework (ABBDAC) for real-time attribute tokens in the cloud, enabling collaborative attribute management by multiple authorities. ABBDAC utilizes smart contracts to create encryption policies, attribute tokens for each attribute center, and render policy decisions, thereby diminishing the communication and computation burdens on data users. Furthermore, BC technology aids in securely documenting access control procedures in an auditable manner. Subsequently, a security analysis of the algorithm is conducted, and the system performance is evaluated. Experimental findings suggest that the proposed scheme is both dependable and easily implementable.

Recent research findings suggest that machine learning models are highly susceptible to backdoor poisoning attacks. Backdoor poisoning attacks can be easily executed and achieve high success rates, as the model exhibits anomalous behavior even if a small quantity of malicious data is incorporated into the training dataset. In conventional backdoor defense technologies, fine-tuning is employed as an invasive method that involves adjusting the parameters of model neurons to eliminate backdoors in the attacked model. Nevertheless, this method poses a challenge as the same neurons are responsible for both the original and backdoor tasks, resulting in a decline in the accuracy of the original task during the fine-tuning process. In order to address this issue, we propose a non-invasive approach known as Anti-Backdoor Model (ABM), which does not involve modifying the parameters of the attacked model. ABM employs an external model to counteract the influence of the backdoor task on the attacked model, thereby achieving a balance between eliminating backdoors and preserving the accuracy of the original task. Specifically, our approach involves initially embedding a controllable backdoor in the dataset and leveraging the strong and weak relationships between backdoors to identify a highly concentrated poisoned dataset. Subsequently, we employ the standard training method to train the attacked model (the teacher model). Finally, we utilize this dataset with low volume to train an external model (the student model) that exclusively focuses on backdoors by means of knowledge distillation to counteract the backdoor task in the attacked model (the teacher model). In the experimental part, we assess the effectiveness of ABM by testing eight mainstream attacks on three standard public datasets. Experimental results reveal that ABM exhibits promising efficacy in eliminating the backdoor task while preserving the accuracy of the original task. Our source codes are open athttps://gitee.com/dugu1076/ABM.git.

Summary Currently, the protection of users' location privacy, particularly for moveable users, is a major concern for both academia and business. In order to receive required services, a moveable user needs to constantly disclose his/her location information with an untrusted third party in his/her locations, which raises security and privacy issues. To settle the above problems, in this work, we creatively integrate local differential privacy (LDP) with conditional random field (CRF) to facilitate continuous location sharing among moveable users. Firstly, we advance a novel approach of employing CRF to represent users' mobility. After that, we establish a system to provide continuous location sharing by combining the ‐location set and ‐LDP. Finally, we evaluate the system performance on actual data sets. The experimental results indicate that our technique outperforms the planar isotropic mechanism (PIM) and AGENT.

Indoor positioning service (IPS) allows users to navigate in large and unfamiliar indoor venues with no or unreliable GPS signals. Received Signal Strength (RSS)-based IPS has received much attentions during the past decade because it does not require costly infrastructure update but makes use of WiFi infrastructure readily available and ubiquitous smartphones. While IPS can greatly facilitate human indoor activities, it also raises serious privacy concerns as periodical location queries submitted by users allow the IPS server to continuously track users’ whereabout. In addition, a curious user may infer the fingerprint database stored at the IPS server. This paper introduces PriHorus, a privacy-preserving indoor positioning scheme built upon Horus, a representative RSS-based indoor positioning system. PriHorus can protect both users’ location privacy and IPS server’s data privacy while achieving the same level of location accuracy as in the original Horus system.

The development of smart cities and deep learning technology is changing our physical world to a cyber world. As one of the main applications, the Internet of Vehicles has been developing rapidly. However, privacy leakage and delay problem for data collection remain as the key concerns behind the fast development of the cyber intelligence technologies. If the original data collected are directly uploaded to the cloud for processing, it will bring huge load pressure and delay to the network communication. Moreover, during this process, it will lead to the leakage of data privacy. To this end, in this article we design a data collection and preprocessing scheme based on deep learning, which adopts the semisupervised learning algorithm of data augmentation and label guessing. Data filtering is performed at the edge layer, and a large amount of similar data and irrelevant data are cleared. If the edge device cannot process some complex data independently, it will send the processed and reliable data to the cloud for further processing, which maximizes the protection of user privacy. Our method significantly reduces the amount of data uploaded to the cloud, and meanwhile protects the user's data privacy effectively.

As an enabler for smart industrial Internet of Things (IoT), sensor cloud facilitates data collection, processing, analysis, storage, and sharing on demand. However, compromised or malicious sensor nodes may cause the collected data to be invalid or even endanger the normal operation of an entire IoT system. Therefore, designing an effective mechanism to ensure the trustworthiness of sensor nodes is a critical issue. However, existing cloud computing models cannot provide direct and effective management for the sensor nodes. Meanwhile, the insufficient computation and storage ability of sensor nodes makes them incapable of performing complex intelligent algorithms. To this end, mobile edge nodes with relatively strong computation and storage ability are exploited to provide intelligent trust evaluation and management for sensor nodes. In this article, a mobile edge computing-based intelligent trust evaluation scheme is proposed to comprehensively evaluate the trustworthiness of sensor nodes using probabilistic graphical model. The proposed mechanism evaluates the trustworthiness of sensor nodes from data collection and communication behavior. Moreover, the moving path for the edge nodes is scheduled to improve the probability of direct trust evaluation and decrease the moving distance. An approximation algorithm with provable performance is designed. Extensive experiments validate that our method can effectively ensure the trustworthiness of sensor nodes and decrease the energy consumption.

Under the combination of Internet-of-Things (IoT) technology and traditional industry, the Industrial IoT (IIoT) came into being and received wide attention from all walks of life. With the increase of the number of IIOT devices in industrial environments, security threats, and quality of service (QoS) issues increase drastically. Internal attack is one type of important security threat that makes service environment worse and less reliable. However, there is no unified and fine-grained trust evaluation mechanism to deal with the threats of internal attack and improve QoS of IIoT. To this end, a unified trustworthy environment based on edge computing is established and maintained, which can timely detect malicious service providers and service consumers, filter unreal information, and recommend credible service providers. Edge computing is introduced as an effective service access point, since it supports collecting service records to perform trust evaluations. Moreover, a service selection method is designed to choose the corresponding trustworthy and reliable service providers based on the trust evaluation and the recording criterion, which has distinctive advantages in the succinct trust management, convenient searching service, and accurate service matching. Experiments validated the feasibility of the proposed trustworthy environment.

Abstract With the development of Lie theory, Lie groups have attained profound significance in several branches of Mathematics and Physics. In Lie theory, the matrix exponential plays a crucial role between Lie groups and Lie algebras. Meanwhile, as the finite analogue of Lie groups, finite groups of Lie type have potential applications in cryptography due to their unique mathematical structures. In this paper, we first put forward a novel idea of designing cryptosystems based on Lie theory. First of all, combing with discrete logarithm problem and group factorization problem, we proposed several new intractable assumptions based on the matrix exponential in finite groups of Lie type. Subsequently, in analog with Boyen’s scheme (Asiacrypt 2007), we designed a public-key encryption scheme based on the non-abelian factorization problem in finite groups of Lie type. Finally, our proposal was proved to be indistinguishable against adaptively chosen-ciphertext attack in the random oracle model. It is encouraging that our scheme also has the potential to resist against Shor’s quantum algorithm attack.

Both academia and industry have directed tremendous interest toward the combination of Cyber Physical Systems and Cloud Computing, which enables a new breed of applications and services. However, due to the relative long distance between remote cloud and end nodes, Cloud Computing cannot provide effective and direct management for end nodes, which leads to security vulnerabilities. In this article, we first propose a novel trust evaluation mechanism using crowdsourcing and Intelligent Mobile Edge Computing. The mobile edge users with relatively strong computation and storage ability are exploited to provide direct management for end nodes. Through close access to end nodes, mobile edge users can obtain various information of the end nodes and determine whether the node is trustworthy. Then, two incentive mechanisms, i.e., Trustworthy Incentive and Quality-Aware Trustworthy Incentive Mechanisms, are proposed for motivating mobile edge users to conduct trust evaluation. The first one aims to motivate edge users to upload their real information about their capability and costs. The purpose of the second one is to motivate edge users to make trustworthy effort to conduct tasks and report results. Detailed theoretical analysis demonstrates the validity of Quality-Aware Trustworthy Incentive Mechanism from data trustfulness, effort trustfulness, and quality trustfulness, respectively. Extensive experiments are carried out to validate the proposed trust evaluation and incentive mechanisms. The results corroborate that the proposed mechanisms can efficiently stimulate mobile edge users to perform evaluation task and improve the accuracy of trust evaluation.

Summary Network reprogramming faces lots of threats from both external attackers and potentially compromised nodes. Security thus becomes a critical requirement for network reprogramming protocols. This paper describes a secure network reprogramming system called SecNRCC for dynamically reprogramable wireless sensor network. In SecNRCC, a light weight authentication method is firstly introduced for the reboot control command. Secondly, a program image preprocess method with security and loss‐tolerance consideration is proposed. Furthermore, a novel immediate packet authentication algorithm with confidentiality consideration is also presented to resist the denial of service attacks exploiting the authentication delay, and finally, a weak authentication operation is performed before the digital signature verification to mitigate denial of service attacks against signature packets. The experimental results show that SecNRCC can securely disseminate the program image to all of node in the wireless sensor networks with acceptable latency and message cost. Copyright © 2014 John Wiley & Sons, Ltd.

This paper proposes a new cryptographic primitive, named identity-based conditional proxy re-encryption (IBCPRE). In this primitive, a proxy with some information (a.k.a. re-encryption key) is allowed to transform a subset of ciphertexts under an identity to other ciphertexts under another identity. Due to the specific transformation, IBCPRE is very useful in encrypted email forwarding. Furthermore, we propose a concrete IBCPRE scheme based on Boneh-Franklin identity-based encryption. The proposed IBCPRE scheme is secure against the chosen ciphertext and identity attack in the random oracle.

To construct a suitable and secure proxy re-signature scheme is not an easy job, up to now, there exist only a few schemes. None of these schemes is unidirectional identity-based proxy re-signature, where a semi-trusted proxy can transform a signature under an identity to another signature under another identity on the same message, while the proxy cannot generate any signature on behalf of any of these two identities. In this paper, based on Schnorr's signature and Libert-Vergnaud proxy re-signature, we propose the first unidirectional identity-based proxy re-signature, which is existentially unforgeable in the random oracle model based on the extended computational Diffie-Hellman assumption.