With the advancement of deep learning, DNNs have been widely deployed across diverse domains. Model-as-a-Service (MaaS) platforms allow enterprises to commercialize well-trained models, which are built through extensive data collection and substantial computational investment. Consequently, protecting these models from unauthorized use and intellectual property (IP) theft has become critical. While watermarking has emerged as a prominent IP protection technique, most existing approaches target centralized settings, leaving federated learning (FL) scenarios largely underexplored. To bridge this gap, we propose Federated Watermarking with Distributed Verification (FWDV), a novel framework tailored for FL. FWDV enables each client to independently verify watermark ownership and jointly defend the model against erasure attempts. To our knowledge, this is the first work to achieve both distributed verification and robustness against a broad spectrum of attacks. Extensive experiments demonstrate that FWDV embeds watermarks with minimal impact on model utility and resists removal through fine-tuning, pruning, and distillation.

With the rising demand for parking spaces, effective management and allocation of parking resources have become increasingly crucial. Traditional intelligent transportation systems often rely on various sensors to gather parking space information, which is then transmitted to centralized servers for data analysis and storage. This approach poses security risks, such as single points of failure and system strain during peak periods, due to increased data transmission and processing. Additionally, the surge in user activity can degrade system performance. To address these issues, this article introduces a parking space search system that records parking information on a blockchain to ensure data integrity and incorporates fine-grained permission management. By utilizing threshold attribute-based encryption, our approach effectively reduces system burden during peak periods. Experimental results show that our system achieves a delay reduction ranging from 22% to 85.6% compared to the current schemes as the number of vehicles increases. These results underscore the practical security and feasibility of our proposed solution.

Neural network (NN) compression aims at reducing the model size and receives much research attention. Nevertheless, we observe that when compressing convolutional neural networks (CNNs), previous approaches may not well measure the impact of filters to loss, resulting in a significant performance degradation after compression. On the other hand, for compressing the fully connected neural networks (FCNNs), we observe that converting the weight matrix to the block diagonal structure would result in better compression. Therefore, for compressing CNNs, we propose a new pipeline in this article, named Retraining-Aware Pruning (RAP) , with a new self-distillation approach, named High-Level Activation-Guided Attention-Preserving Self-Distillation (HAP) and a novel filter pruning strategy, named Normalized Gradients and Geometric Median (NGGM) to effectively improve the accuracy and reduce the model size. Further, for reducing the model size of FCNNs, we formulate a new research problem, i.e., Compression with Difference-Minimized Block Diagonal Structure (COMIS) , and propose a new algorithm, Memory-Efficient and Structure-Aware Compression (MESA) to effectively prune the weights into a block diagonal structure to significantly boost the compression rate. Extensive experiments on different models show that our approaches significantly outperform the state-of-the-art baselines in terms of compression rate, accuracy, and inference speed-up.

Recently, many machine learning-based approaches that effectively solve graph optimization problems have been proposed. The graph optimization problem is the problem that aims to optimize (maximize or minimize) a quantity that is associated with a graph, such as the Minimum Vertex Cover (MVC) and Maximum Independent Set (MIS) problems. These approaches are usually trained on graphs randomly generated with graph generators or sampled from existing datasets. However, we observe that such training graphs lead to poor testing performance if the testing graphs are not generated analogously, i.e., the generalizability of the models trained on thoserandomly generatedtraining graphs is very limited. To address this critical issue, in this paper, we propose a new framework, namedLearning with Iterative Graph Diversification (LIGD), and formulate a new research problem, namedDiverse Graph Modification Problem (DGMP), that iteratively generate diversified training graphs and train the models that solve graph optimization problems to improve their performance significantly. We propose three approaches to solve DGMP by considering both the performance of the machine learning approaches and the structural properties of the training graphs. In addition, we study a practical case of DGMP, namedDiverse Graph Modification Problem with XOR Diversity (DGMP-XDiv), which considers an XOR-based diversity function. We propose a polynomial-time algorithm namedStructure Diversifying Modification on Edge Score (DMES)to obtain the optimal solution. We also proposeDMES with Efficiency-Boosting Strategies (DMES-EB)to enhance the efficiency of DMES significantly. Experimental results on well-known problems show that our proposed approaches significantly boost the performance of both supervised and reinforcement learning approaches. They produce near-optimal results and significantly outperform the baseline approaches, such as graph augmentation and diffusion-based approaches.

In recent years, the notion of federated learning (FL) has led to the new paradigm of distributed artificial intelligence (AI) with privacy preservation. However, most current FL systems suffer from data privacy issues due to the requirement of a trusted third party. Although some previous works introduce differential privacy to protect the data, however, it may also significantly deteriorate the model performance. To address these issues, we propose a novel decentralized collaborative AI framework, named Auditable Homomorphic-based Decentralised Collaborative AI (AerisAI), to improve security with homomorphic encryption and fine-grained differential privacy. Our proposed AerisAI directly aggregates the encrypted parameters with a blockchain-based smart contract to get rid of the need of a trusted third party. We also propose a brand-new concept for eliminating the negative impacts of differential privacy for model performance. Moreover, the proposed AerisAI also provides the broadcast-aware group key management based on ciphertext-policy attribute-based encryption (CP-ABE) to achieve fine-grained access control based on different service-level agreements. We provide a formal theoretical analysis of the proposed AerisAI as well as the functionality comparison with the other baselines. We also conduct extensive experiments on real datasets to evaluate the proposed approach. The experimental results indicate that our proposed AerisAI significantly outperforms the other state-of-the-art baselines.

Graph neural networks (GNNs) enable many novel applications and achieve excellent performance. However, their performance may be significantly degraded by the graph adversarial attacks, which intentionally add small perturbations to the graph. Previous countermeasures usually handle such attacks by enhancing model robustness. However, robust models cannot identify thetarget nodesof the adversarial attacks, and thus we are unable to pinpoint the weak spots and analyze the causes or the targets of the attacks. In this article, we study the important research problem to detect thetarget nodesof graph adversarial attacks under theblack-box detectionscenario, which is particularly challenging because our detection models do not have any knowledge about the attacker, while the attackers usually employ unnoticeability strategies to minimize the chance of being detected. To our best knowledge, this is the first work that aims at detecting thetarget nodesof graph adversarial attacks under theblack-box detectorscenario. We propose two detection models, namedDet-HandDet-RL, which employ different techniques that effectively detect the target nodes under the black-box detection scenario against various graph adversarial attacks. To enhance the generalization of the proposed detectors, we further propose two novel surrogate attackers that are able to generate effective attack examples and camouflage their attack traces for training robust detectors. In addition, we propose three strategies to effectively improve the training efficiency. Experimental results on multiple datasets show that our proposed detectors significantly outperform the other baselines against multiple state-of-the-art graph adversarial attackers with various attack strategies. The proposedDet-RLdetector achieves an averaged area under curve (AUC) of 0.945 against all the attackers, and our efficiency-improving strategies are able save up to 91% of the training time.

The use of IoT technology in collecting personal health records (PHR) within the eHealth environment is a growing trend. However, data integrity is a concern as cloud service providers (CSPs) often cannot guarantee it. Blockchain technology offers a solution to guarantee data integrity and traceability. However, the immutability of traditional blockchain conflicts with GDPR's requirements. To address scalability and privacy concerns, we have designed a comprehensive scheme that integrates the redactable blockchain with the existing revocable IPFS mechanism. Our scheme overcomes the disadvantage of residual downloading information in the traditional blockchain. Additionally, we have developed an enhanced proxy re-encryption scheme that simplifies access control for physicians without the need for complex group key management. Unlike traditional blockchains and P2P file sharing systems, our PHR platform allows for selective removal of records and files while maintaining auditable logs. Evaluation results demonstrate that our proposed scheme effectively enhances the exclusive revocation feature with acceptable overheads. To the best of our knowledge, this is the first work to provide the merit of fully complete record and file revocation on a blockchain-based system.

Egocentric network (ego network) data are very important for evaluating algorithms and machine learning approaches in Online Social Networks (OSNs). Nevertheless, obtaining the ego network data from OSNs is not a trivial task. Conventional manual approaches are time-consuming, and sometimes the ego network data are quite incomplete because only a small number of users would agree to provide their data. This is because there are two important factors that should be considered simultaneously for this data acquisition task: i) users’ willingness to provide their data, and ii) the structure of the ego network. However, addressing the above two factors to obtain the more complete ego network data has not received much research attention. Therefore, in this paper, we address this issue by proposing a family of new research problems. The first proposed problem, namedWillingness Maximization for Ego Network Extraction in Online Social Networks (WMEgo), identifies a set of ego networks from a single OSN, such that the willingness of the users to provide their data is maximized. We prove that WMEgo is NP-hard and propose a$\frac{1}{2}(1-\frac{1}{e})$-approximation algorithm, namedEgo Network Identification with Maximum Willingness (EIMW). Furthermore, we extend the idea of WMEgo to multiple social networks and formulate a new research problem, namedWillingness Maximization on Multiple Social Networks for Ego Network Extraction (WM$^{2}$2Ego), which is able to effectively obtain ego network data from multiple social networkssimultaneously. We propose a$\frac{1}{2}$-approximation algorithm, namedMaximum Expansion forUNifiedEXpenses (MUNEX)for a special case of WM$^{2}$Ego and then design a constant-ratio approximation algorithm to the general WM$^{2}$Ego problem, namedMaximum Expansion with Expense Examination (M3E). We conduct two evaluation studies with 672 and 1,052 volunteers to validate the proposed WMEgo and WM$^{2}$Ego problems, respectively, and show that they are able to obtain much more complete ego network data compared to other baselines. We also perform extensive experiments on multiple real datasets to demonstrate that the proposed approaches significantly outperform the other baselines.

For machine learning in online social networks or mobile environments, data privacy-preserving is a very important issue. In the past, artificial intelligence and machine learning algorithms that operating on a stand-alone machine must analyze each data in order to build an accurate model when training a model. However, this method is insufficient in terms of privacy protection, because it must collect data from different sources; on the other hand, if these algorithms analyze only a part of the data, the accuracy of the models constructed by artificial intelligence and machine learning algorithms will be very low. To solve the above problems, this study starts from the privacy protection aspect of industrial data management, combined with the concept of federated learning, and hopes to improve the accuracy of algorithm modules while ensuring data privacy. Therefore, in this paper, we analyze the impact of federated learning under different protocol mechanisms on the accuracy of algorithm modules, and based on this, we can apply this module to integrate blockchain smart contract technology in the future.

Finding a set of suitable experts with minimized communication overhead to perform a complex task finds a wide spectrum of applications in industry, education, and other scenarios. This class of problems, widely formulated as forming a team of experts in social networks (i.e., team formation problem), is very challenging due to its NP-hardness and has attracted much research attention. Although various effective and elegant algorithms have been proposed to address this important problem, the methods are usually manually designed and handcrafted, which require considerable human efforts. In this article, we make our first attempt to automate the algorithm design with a machine learning-based approach, named reinforcement learning-based expert team identification (RELEXT). Moreover, we also propose two novel graph embedding methods to consider two important dimensions of the team formation problem, i.e., the skill and social dimensions. We evaluate the proposed approaches on multiple large-scale real datasets. The experimental results show that our proposed approaches outperform the other baselines in terms of solution quality and efficiency.

Intelligent Transport Systems (ITSs) play an important role in future smart city design to improve traffic safety and traffic congestion by sharing data collected by vehicles. For sharing the traffic data with other vehicles, the vehicular sensory data are usually uploaded to the cloud server. However, existing data sharing systems for VANETs cannot provide selective data with sufficient privacy protection. Moreover, some schemes also cannot ensure stable data accessibility and the integrity of retrieved data. On the other hand, with the improvements such as lower latency, higher capacity, and increased bandwidth, 5G technology brings more possibilities to future applications. The join of the software-defined networks (SDNs) also offers efficient and effective network management. This paper proposes a primitive vehicular communication system named blockchain-based privacy-preserving and sustainable data query service. The proposed scheme is designed to realize stable data accessibility by leveraging smart contracts and blockchain oracle. With the help of 5G technology and P2P file-sharing system, InterPlanetary File System (IPFS), the proposed scheme aims to support video downloading files with searchable capability and fairness. An incentive token mechanism is also equipped. The merit of auditability is ensured by Ethereum blockchain platform to support the accountability. Besides, we also evaluate its networking performance via SUMO and NS-3 simulators. Our simulation results show that the request-response delay of BPSDQS is less than existing blockchain-based proxy re-encryption (PRE) scheme. Our simulation results also showed that the average request-response delay in our scheme can saving up to 98%.

With the rapid growth of blockchain, more and more people possess cryptocurrencies or use decentralized applications (Dapp). A wallet in blockchain not only stores your assets but also represents your online identity. Nowadays, document notary using blockchain is getting mature to prevent the problem of the counterfeit certificate. In this paper, we integrate a cellphone-based wallet with a novel visional certificate verification system. By the trusted execution environment (TEE) protection, a user can isolate his/her private key without the leakage attack. Two-factor authentication is required to generate a signature. Different from existing platforms, our system adopts IPFS, a P2P network for storing and sharing data, for storing the certificate image, which offers the function of visional verification for better persuasiveness. Furthermore, the unique time-limited verification can restrict the accessing period of the verifier for better privacy protection. As a result, our verification system provides several promising features to enhance security and privacy strength.

Package promotion (or product bundling and bundle promotion) is widely adopted as an effective marketing strategy to increase sales, but the social tightness of the users significantly influences their willingness to purchase certain products. However, addressing these two factors simultaneously is not a trivial task because it is critical to properly choose a set of socially tight target users to encourage them to buy the products together (social tightness factor), and the selected users should have high preference for the package of products (preference factor). To address the aforementioned challenges, in this article, we study the research problem of promoting a package of products to a set of closely related friends. We formulate a new research problem, named package-oriented group identification (PGI), which can obtain a set of t socially tight users (i.e., inducing more than k edges) who have the maximum preference for a package of items. We prove that the proposed PGI problem is NP-hard, and we develop a polynomial-time algorithm named incremental solution construction with redundancy and infeasibility avoidance for PGI (ISCP) that can effectively and efficiently obtain a good solution to the PGI problem. We compare the performance of ISCP with four other baselines on a large-scale product copurchasing data set with more than 500 k products and 1.7 M copurchasing relationships. The results show that our proposed ISCP algorithm outperforms the other baselines in terms of solution quality and efficiency.

With the rise of blockchain technology, peer-to-peer network system has once again caught people's attention. Peer-to-peer (P2P) is currently being implemented on various kind of decentralized systems such as InterPlanetary File System (IPFS). However, P2P file sharing network systems is not without its flaws. Data stored in the other nodes cannot be deleted by the owner and can only be deleted by other nodes themselves. Ensuring that personal data can be completely removed is an important issue to comply with the European Union's General Data Protection Regulation (GDPR) criteria. To improve P2Ps privacy and security, we propose a monitorable peer-to-peer file sharing mechanism that synchronizes with other nodes to perform file deletion and to generate the File Authentication Code (FAC) of each IPFS nodes in order to make sure the system synchronized correctly. The proposed mechanism can integrate with a consortium Blockchain to comply with GDPR.

With advance of IoT (Internet of Things) technology, many manufacturers install several sensors to monitor the status of machines and the health of the whole manufacturing process. In addition, the sensed data are usually transmitted to a backend database for further analysis. However, the dramatic volume of data sensed by the sensors causes the problem of huge storage requirement and network traffic for the small medium manufacturers which have limited resource and budget in IT (Information Technology). To deal with this problem, we design a two-layered architecture using compression technique to reduce the network traffic. In addition, we use MongoDB, a NoSQL database, to store the compressed data due to MongoDB's excellent scale-out ability and cost-efficiency. We conduct several experiments to measure the performance of the proposed architecture with several compression methods. Experimental results show that with proper lossless compression method, the reduction ratio of the volume of the data is around 80% at the cost of slight increase in execution time.

Thanks to the growth of cloud computing and network technology, different organizations might want to share data and resources between each other. However, cross-organizational authentication systems usually need a central control system, which must be fully trusted. Thus, we use blockchain technology to store the access control list of users due to its tamper-proof and decentralized feature. Our system also provides authentication/authorization/accounting functions by using a virtual coin exe_coin to achieve accounting function. The method of one-way hash chain is used to securely adapt to the transparency feature of blockchain. In authentication system, the transparency may lead to the linkability problem. In our scheme, attackers cannot get the linkability between the transactions and the particular user. To the best of our knowledge, our scheme is the first blockchain-based authentication system with the merits of unlinkability and accounting.

The eHealth trend has spread globally. Internet of Things (IoT) devices for medical service and pervasive Personal Health Information (PHI) systems play important roles in the eHealth environment. A cloud-based PHI system appears promising but raises privacy and information security concerns. We propose a cloud-based fine-grained health information access control framework for lightweight IoT devices with data dynamics auditing and attribute revocation functions. Only symmetric cryptography is required for IoT devices, such as wireless body sensors. A variant of ciphertext-policy attribute-based encryption, dual encryption, and Merkle hash trees are used to support fine-grained access control, efficient dynamic data auditing, batch auditing, and attribute revocation. Moreover, the proposed scheme also defines and handles the cloud reciprocity problem wherein cloud service providers can help each other avoid fines resulting from data loss. Security analysis and performance comparisons show that the proposed scheme is an excellent candidate for a cloud-based PHI system.

Emergency response times following a traffic accident are extremely crucial in reducing the number of traffic-related deaths. Existing emergency vehicle dispatching systems rely heavily on manual assignments. Although some technology-assisted emergency systems engage in emergency message dissemination and path planning, efficient emergency response is one of the main factors that can decrease traffic-related deaths. Obviously, effective emergency response often plays a far more important role in a successful rescue. In this article, we propose a secure IoT-based and incentive-aware emergency personnel dispatching scheme (EPDS) with weighted fine-grained access control. Our EPDS can recruit available medical personnel on-the-fly, such as physicians driving in the vicinity of the accident scene. An appropriate incentive, such as paid leave, can be offered to encourage medical personnel to join rescue missions. Furthermore, IoT-based devices are installed in vehicles or wearable on drivers to gather biometric signals from the driver, which can be used to decide precisely which divisions or physicians are needed to administer the appropriate remedy. Additionally, our scheme can cryptographically authorize the assigned rescue vehicle to control traffic to increase rescue efficacy. Our scheme also takes advantage of adjacent roadside units to organize the appropriate rescue personnel without requiring long-distance communication with a trusted traffic authority. Proof of security is provided and extensive analyses, including qualitative and quantitative analyses and simulations, show that the proposed scheme can significantly improve rescue response time and effectiveness. To the best of our knowledge, this is the first work to make use of medical personnel that are close by in emergency rescue missions.

To support the high mobility of vehicles, the Internet Engineering Task Force (IETF) defines proxy mobile IPv6 (PMIPv6) to reduce the signaling overhead. However, the design of PMIPv6 does not thoroughly consider security issues, such as man-in-the-middle and impersonation attacks. Moreover, the traditional authentication/authorization/accounting (AAA) server architecture in PMIPv6 could impede the localized advantage because of the long-distance delivery between a mobile access gateway (MAG) and the AAA server. In practice, the billing is a crucial issue that is, unfortunately, rarely discussed in vehicular ad hoc networks (VANETs). In this paper, a local-based authentication and billing scheme is proposed to lessen the long-distance communication overhead. An incentive-aware multihop forwarding procedure is also offered to stimulate the help of forwarding others' messages in a vehicle-to-vehicle (V2V) environment. Therefore, the proposed billing scheme is designed for full VANETs, including the vehicle-to-infrastructure (V2I) and V2V environments. Lightweight keyed hash functions and batch verification are employed for efficient computation and concise communication overhead. Only a few signatures are used in the first message to ensure the nonrepudiation payment approval. Security analysis and performance evaluation show that the proposed scheme is secure and efficient, compared with a conventional public-key based scheme. The advantages of the proposed scheme include: 1) mutual authentication and session key agreement; 2) privacy preservation; 3) confidentiality, integrity, free-riding resistance, double-spending avoidance, and nonrepudiation properties; and 4) efficient billing and payment clearance.

Vehicular ad hoc networks (VANETs) are an emerging wireless network technology used to improve road safety. Commercial services will play an important role in drawing customers to VANETs. Therefore, service-oriented vehicular networks offer an effective and promising approach. To meet the diverse requirements of different users, fine-grained access control is essential. This paper aims to address security, privacy and billing issues in service-oriented vehicular networks. Taking advantage of a portable electronic currency, the proposed scheme mitigates the long authentication delay of the centralized AAA architecture. Variant attribute-based encryption ensures fine-grained access control and secure billing. Only vehicles possessing the proper service attributes and valid electronic currency are authorized to access the requested service file. The security properties of entity authentication, session key agreement, privacy, fraud electronic currency prevention, double-spending prevention, and nonrepudiated billing are achieved. Extensive analysis and simulations demonstrate that our scheme is a viable candidate to replace a centralized AAA architecture with a decentralized method for better scalability in service-oriented vehicular networks.

Current mobile pay-TV systems have two types of Conditional Access Systems (CAS): group-key-based and public-key systems. The best feature of group-key-based systems is the ability to enjoy the broadcast nature in delivery multimedia contents, while the major advantage of public-key systems is consolidating the security foundation to withstand various attacks, such as collusion attacks. However, the problems of group-key-based systems include collusion attacks, lack of nonrepudiation, and troublesome key distribution. Even worse, the benefit of broadcast efficiency is confined to a group size of no more than 512 subscribers. For public-key systems, the poor delivery scalability is the major shortcoming because the unique private key feature is only suitable for one-to-one delivery. In this article, we introduce a scalable access control scheme to integrate the merits of broadcasting regardless of group size and sound security assurance, including fine-grained access control and collusion attack resistance. For subscriber revocation, a single message is broadcast to the other subscribers to get the updated key, thus significantly boosting subscriber revocation scalability. Due to mobile subscribers' dynamic movements, this article also analyzes the benefit of retransmission cases in our system. Through the performance evaluation and functionality comparison, the proposed scheme should be a decent candidate to enhance the security strength and transmission efficiency in a mobile pay-TV system.

For a better compatibility, proxy mobile IPv6 (PMIPv6) has been proposed as a network-based mobility management protocol without the requirement of the participation of mobile terminals. With the feature of localized mobility, PMIPv6 is often used in vehicular ad hoc networks (VANETs) to suit for the high mobility property in vehicles. However, the design of PMIPv6 does not consider security issues thoroughly. The identity authentication of a mobile terminal is suggested by an authentication/authorization/accounting (AAA) server architecture in PMIPv6 standard, which damages the localized advantage because of the long transmission distance between mobile access gateways (MAGs) and the AAA server. In this paper, we propose a localized authentication and billing scheme to reduce the communications between MAGs and the AAA server. Moreover, our billing function enables an Internet Service Providers (ISP) to easily charge mobile terminals (MTs) even if these MTs are roaming within other ISPs' networks. The techniques of the signcryption, hash chain and batch verification are adopted to achieve multiple advantages including (1) mutual authentication and session key agreement, (2) privacy preserving, (3) confidentiality, integrity, and non-repudiation properties, (4) efficient billing and payment clearance.

Recently, Sun and Leu proposed an efficient authentication scheme for access control in mobile pay-TV systems based on pairing and elliptic curve cryptography. In Sun and Leu's scheme, there exist two fatal security flaws, including 1) failure in subscriber authentication and 2) vulnerable to unauthorized access, which contradict to the security claims of Sun and Leu's scheme. To meet the claimed security requirements, we strengthen Sun and Leu's scheme with only lightweight modifications.

In this paper, we propose an Attribute-Based Access Control System (ABACS) for emergency services with security assurance over Vehicular Ad Hoc Networks (VANETs). ABACS aims to improve the efficiency of rescues mobilized via emergency communications over VANETs. By adopting fuzzy identity-based encryption, ABACS can select the emergency vehicles that can most appropriately deal with an emergency and securely delegate the authority to control traffic facilities to the assigned emergency vehicles. Using novel cryptographic preliminaries, ABACS realizes confidentiality of messages, prevention of collusion attacks, and fine-grained access control. As compared to the current PKI scheme, the computational delay and transmission overhead can be reduced by exploiting the advantages afforded by message broadcasting, which is heavily used in ABACS. The performance evaluation demonstrates that ABACS is a suitable candidate for realizing emergency services via VANETs.

Abstract The use of anonymous channel tickets was proposed for authentication in wireless environments to provide user anonymity and to probably reduce the overhead of re‐authentications. Recently, Yanget al.proposed a secure and efficient authentication protocol for anonymous channel in wireless systems without employing asymmetric cryptosystems. In this paper, we will show that Yanget al.'s scheme is vulnerable to guessing attacks performed by malicious visited networks, which can easily obtain the secret keys of the users. We propose a new practical authentication scheme not only reserving the merits of Yanget al.'s scheme, but also extending some additional merits including: no verification table in the home network, free of time synchronization between mobile stations and visited networks, and without obsolete anonymous tickets left in visited networks. The proposed scheme is developed based on a secure one‐way hash function and simple operations, a feature which is extremely fit for mobile devices. We provide the soundness of the authentication protocol by using VO logic. Copyright © 2010 John Wiley & Sons, Ltd.