Heterogeneous graph neural networks (HGNNs) have witnessed remarkable progress and widespread applications in recent years. Meanwhile, there is growing attention regarding their vulnerability to adversarial attacks. Existing attack methods for HGNNs generate perturbations to slightly modify the structure of a heterogeneous graph, thereby degrading the predictive performance of HGNNs on target nodes. However, to craft such a perturbation, these methods require solving a complicated optimization problem, which makes them computationally inefficient for launching attacks during the inference phase. In this work, we, therefore, introduce generative heterogeneous attack (GHAttack), a novel generative attack method for efficient and effective adversarial attacks on HGNNs. Specifically, GHAttack aims to train a perturbation generator, which produces a perturbation for each target node via a simple forward pass, while allowing the perturbation to modify edges on the heterogeneous relations of the graph to obtain high attack effectiveness. To achieve this, we design a novel model architecture for the generator, consisting of an HGNN backbone and a relation-aware output layer. We formulate the training of the generator as an optimization problem and efficiently solve it by addressing a series of technical challenges. Extensive experiments on ten representative HGNNs and six datasets verify the high efficiency and excellent effectiveness of GHAttack.

Federated Class-Incremental Learning (FCIL) enables dynamic model updates, but suffers from local and global catastrophic forgetting due to limited client storage and cross-client class non-i.i.d. issues. To address catastrophic forgetting, we propose a multi-scale FCIL scheme, DLA-FCIL, which incorporates a Double-Loss-assisted forgetting compensation mechanism and the Auxiliary generators based on data characteristics. Specifically, to mitigate local catastrophic forgetting, we incorporate an auxiliary generator on local clients for knowledge replay, augmenting the training datasets with generated samples to form hybrid datasets. Then, to fully leverage the hybrid datasets, we design a double-loss forgetting compensation mechanism. This mechanism includes a gradient-weighted compensation loss that normalizes forgetting rates across old class knowledge, and a semantic-transition compensation loss that extracts the semantic relationships between old and new classes, preventing abrupt shifts in semantic consistency during the class transitions. Besides, to effectively alleviate the catastrophic forgetting problem caused by global class imbalance, the trained auxiliary generator is sent to a proxy server with minimal communication cost to build an i.i.d. dataset, enabling the development of an optimal global model. Finally, comparison experiments on CIFAR100, ImageNet-Subset, and Tiny-ImageNet datasets demonstrate that DLA-FCIL consistently outperforms other FCIL baselines by approximately 3–15% in test accuracy.

The sixth-generation (6G) mobile communication networks are perceived as large-scale heterogeneous networks. With their increased heterogenization and densification, it is crucial to guarantee the security and efficiency of user equipment's handovers between networks. However, existing cross-domain handover authentication schemes cannot ensure handover authentication efficiency and cannot balance privacy and system efficiency, which thus cannot be directly applied in heterogeneous networks. In this paper, we present PECHA, a privacy-preserving and efficient cross-domain handover authentication scheme for heterogeneous networks, which enables anonymous authentication on user equipment (UE) through the collision property of chameleon hash functions. PECHA ensures authentication efficiency by employing the interplanetary file system and blockchain to synchronize UE's authentication information to target networks in advance. The privacy and system efficiency are balanced by modeling the unlinkability of UE's new and old chameleon hash values and determining the update frequency of UE chameleon hash value. PECHA also achieves correctness, mutual authentication and key agreement, anonymity, unlinkability, conditional privacy, forward/backward secrecy, robustness, known randomness secrecy, key escrow freeness and rapid response, and resists against spoofing attacks, replay attacks and man-in-the-middle attacks. Comprehensive performance analysis, evaluation and comparisons show that PECHA is efficient with respect to both computation and communication.

Authenticated key agreement (AKA) between vehicles and road side units (RSUs) is crucial in vehicular ad-hoc networks (VANETs). However, existing solutions still suffer from high overheads of AKA and lack a mechanism to balance privacy strength and system efficiency. In this paper, we present a lightweight anonymous authenticated key agreement (LWAKA) scheme for VANETs, supporting lightweight anonymous authentication and key agreement between vehicles and RSUs simultaneously. In particular, vehicles’ authentication information is synchronized to target RSUs in advance for accelerating authentication, and lightweight cryptographic operations (i.e., hash function, hash-based message authentication, physical unclonable function, fuzzy extractor and symmetric encryption) are employed to ensure the high efficiency of AKA in terms of computation and communication overheads. The system efficiency and privacy are balanced through modeling the relationship between the frequency of pseudonym updates and the unlinkability of the vehicles’ new and old pseudonyms. Security analysis shows that LWAKA not only achieves anonymity, conditional privacy, pseudonym unlinkability, key escrow freeness, and physical security, but also resists against most known attacks. Comparative experimental results demonstrate that LWAKA outperforms existing schemes in terms of lightweight design.

Compared with synchronous federated learning (FL), asynchronous FL (AFL) has attracted more and more attention in edge computing (EC) fields because of its strong adaptability to heterogeneous application scenarios. However, the non-independent and identically distributed (Non-IID) data across devices and the staleness-aware estimation of unreliable wireless connections and limited edge resources make it much more difficult to achieve better AFL-related applications. To handle this problem, we propose anAdaptiveStaleness-awareMomentumAcceleratedAFL(ASMAFL) algorithm to reduce the resources consumption of heterogeneous wireless communication EC (WCEC) scenarios, as well as decrease the negative impact of Non-IID data for model training. Specifically, we first introduce the staleness-aware parameter and a unified momentum gradient descent (GD) framework to reformulate AFL. Then, we establish global convergence properties of AFL, derive an upper bound on AFL convergence rate, and find that the bound is related to the staleness-aware parameter and Non-IIDness. Next, we formulate the bound into a minimization problem of resource consumption under given model accuracy, and the corresponding staleness-aware parameter of devices will be recomputed after each asynchronous aggregation to eliminate the differences of local models’ contribution to global model aggregation. Finally, extensive experiments are carried out to validate the superiority of ASMAFL in model accuracy, convergence rate, resources consumption, Non-IID issue, etc.

Personalized federated learning (PFL) combines client needs and data characteristics to train personalized models for local clients. However, the most of previous PFL schemes encountered challenges such as low model prediction accuracy and privacy leakage when applied to practical datasets. Besides, the existing privacy protection methods fail to achieve satisfactory results in terms of model prediction accuracy and security simultaneously. In this paper, we propose a Privacy-preserving Personalized Federated Learning under Secure Multi-party Computation (SMC-PPFL), which can preserve privacy while obtaining a local personalized model with high prediction accuracy. In SMC-PPFL, noise perturbation is utilized to protect similarity computation, and secure multi-party computation is employed for model sub-aggregations. This combination ensures that clients' privacy is preserved, and the computed values remain unbiased without compromising security. Then, we propose a weighted sub-aggregation strategy based on the similarity of clients and introduce a regularization term in the local training to improve prediction accuracy. Finally, we evaluate the performance of SMC-PPFL on three common datasets. The experimental results show that SMC-PPFL achieves 2% ∼ 15% higher prediction accuracy compared to the previous PFL schemes. Besides, the security analysis also verifies that SMC-PPFL can resist model inversion attacks and membership inference attacks

The centralized server in traditional federated learning (FL) is authoritative (i.e. decisive control), which may cause immeasurable damage to the system's security in the event of decision failure or attack. To weaken the authority of the central server, existing studies have proposed blockchain-based federated learning (BFL) approaches. However, existing BFL still suffers from high resource overhead and difficulty in resisting high malicious ratio (more than 50%) attacks. To address the above challenge, this paper proposes an efficient and secure non-authoritative (i.e. highly decentralized) anarchic (i.e. distributed self-governance) federated learning framework which is named NAAFL. During the local process of NAAFL, an area credit-based screening mechanism for participating devices is proposed to ensure that participating devices are always highly trusted devices with higher total credit values. Then, to effectively exclude a high percentage of malicious training gradients, a multi-device validation voting mechanism based on historical information is designed to construct the global gradient. Subsequently, to weaken the central server authority and reduce the resource overhead while guaranteeing security, a secure and low-consumption consensus mechanism based on the federation chain is proposed, and the overhead is further reduced by a momentum acceleration algorithm. Finally, the theoretical analysis and experimental simulation are conducted on the proposed NAAFL. The results further show that the proposed NAAFL outperforms existing studies and can defend against attacks with up to 80% malicious ratio, which exceeds the common threshold (50%) of existing studies. Meanwhile, the overhead of NAAFL is reduced by about 77.51% compared to BFL.

The increasing privacy concerns associated with cloud-assisted image retrieval have captured the attention of researchers. However, a significant number of current research endeavors encounter limitations, including suboptimal accuracy, inefficient retrieval, and a lack of effective result verification mechanisms. To address these limitations, we propose an adaptive verifiable privacy-preserving medical image retrieval (AVPMIR) scheme in the outsourced cloud. Specifically, we utilize the convolutional neural network (CNN) ResNet50 model to extract the feature of each medical image within the dataset of the medical institution, aiming to enhance retrieval accuracy. To enhance retrieval efficiency, we build an encryption searchable index based on a mini-batch$k$-means clustering algorithm. Furthermore, we present an index merging method in which multi-data owners build a different index tree according to different standards. To check the correctness of the returned results from the cloud server, we construct an adaptive verification framework for the obtained results based on chameleon hash and BLS signature. To provide strong security for the medical image datasets, we design an improved logistic chaotic mapping algorithm. The security analysis demonstrates that AVPMIR can defend various threat models. The experiment analysis further indicates that the AVPMIR can improve retrieval efficiency and demonstrate its practicability.

Blockchain is extensively applied to many fields as an immutable distributed ledger. However, the immutability contradicts regulations such as the GDPR ruling “the right to be forgotten” of data. Besides, numerous emerging blockchain-based applications call for elastic data management. To erase some data, redactable blockchains are proposed for breaking the immutability in a controlled way. Unfortunately, the prior solutions may suffer from poor security and centralized control of the redaction privilege. They cannot support dynamic nodes, where the departure of participators will result in a single point of failure. This paper proposes a noveldynamic and decentralizedattribute-basedchameleonhash (DACH) to make blockchain history mutable, achieving asecurely anddynamicallyredactable blockchain (SDR-chain) in a decentralized setting. We first propose the formal definition, security models, and concrete construction of our DACH. Meanwhile, we design a delegation algorithm of DACH to support a dynamically changing committee, where participators can freely and securely leave and join the network. Then, the transactions of the SDR-chain are redacted by computing DACH collisions. The security is analyzed in the random oracle model. Finally, theoretical analysis and experimental evaluation demonstrate that our SDR-chain is superior to the prior solutions in terms of security and functionality.

High-performance traffic flow prediction models provide reliable future road information and optimize traffic navigation systems. However, the traffic data used for model learning contains lots of private information, and the existing privacy-preserving strategies always reduce the accuracy of prediction models. Besides, an effective traffic flow prediction model needs to be continuously and rapidly updated to adapt to dynamic changes in the traffic environment. Thus, we propose a Secure and Efficient Continuous Learning Model (SE-CLM) based on broad learning, spatial correlation, and adaptive sampling processing techniques to realize accurate and efficient traffic flow prediction under strong privacy protection. Specifically, SE-CLM is constructed on the broad network architecture to enable fast and continuous model training. This model is trained on a cloud server by combining the spatial correlation of traffic flows, to achieve accurate traffic flow prediction. Besides, an adaptive sampling strategy is designed to further improve the prediction accuracy of the model under the protection with differential privacy (DP), where the budget allocation for DP is optimized by adaptively sampling traffic flows with different timestamps for noise perturbation processing. Furthermore, the experimental simulations are conducted in real vehicular mobility datasets. The experimental results show that the designed spatial-based SE-CLM achieve more accurate and efficient traffic flow prediction than those of the other existing schemes. The adaptive sampling strategy not only significantly reduces the DP-noise added in traffic flows but also a 20% reduction in communication volume compared to other strategies. Finally, the security analysis also verifies that SE-CLM satisfies w-event ε-DP.

With the widespread use of cloud storage technology by individuals and organizations, data providers usually send their data to cloud for storage to reduce memory pressure, and allow the users to retrieve these data, which has become the trend of rapid data retrieval. To guarantee the data confidentiality, several research works have been developed on encrypted cloud data for ranked multi-keyword retrieval. Nevertheless, most of these schemes are disabled since they cannot resist keyword guessing attacks. Moreover, the ranked top-$K$search results obtained by the subscriber from the encrypted cloud data are inaccurate. To overcome these drawbacks, we design a novel and efficient privacy-preserving ranked multi-keyword retrieval scheme (named as PRMKR) in this paper. With PRMKR, the data and the inverted indexes which belong to the data provider can be securely transferred to the cloud server. In addition, a registered subscriber can request accurate retrieval services without compromising his/her trapdoor information to the cloud server. Specifically, we design an encryption searchable plugin-in server and lower dimensional inverted indexesvector for data owners, which can further guarantee data confidentiality of the data owner and improve search efficiency, respectively. Our rigorous security proof demonstrates that PRMKR can withstand keyword guessing attacks. Finally, experimental evaluations confirm that PRMKR has decent computational and communication efficiency.

With the development of wireless communications and the pervasiveness of location-aware mobile electronic devices, location-based services (LBS) which can provide a convenient lifestyle for people have attracted considerable interest recently. However, there still exists the privacy disclosure problem of LBS today. To solve this problem, in this article, we present a novel privacy-preserving LBS search scheme in outsourced cloud. In the proposed LBS search scheme, the LBS providers data are first outsourced to the cloud server in an encrypted method. Then, a registered user constructs a query model to obtain accurate LBS query results without divulging his/her location information and query attribute to the LBS provider and the cloud server. Specifically, based on the designed matrix encryption technology, the LBS search scheme can achieve privacy preservation of users query and confidentiality of LBS data in the outsourced cloud server. Through security analysis, we show that our scheme can resist various known security threats. The experimental results further show that our LBS search scheme greatly reduces the communication overhead and provides convenient search experience to the users.

Data deduplication can efficiently eliminate data redundancies in cloud storage and reduce the bandwidth requirement of users. However, most previous schemes depending on the help of a trusted key server (KS) are vulnerable and limited because they suffer from revealing information, poor resistance to attacks, great computational overhead, etc. In particular, if the trusted KS fails, the whole system stops working, i.e., single-point-of-failure. In this article, we propose aSecure andEfficient dataDeduplication scheme (named SED) in a JointCloud storage system which provides the global services via collaboration with various clouds. SED also supports dynamic data update and sharing without the help of the trusted KS. Moreover, SED can overcome the single-point-of-failure that commonly occurs in the classic cloud storage system. According to the theoretical analyses, our SED ensures the semantic security in the random oracle model and has strong anti-attack ability such as the brute-force attack resistance and the collusion attack resistance. Besides, SED can effectively eliminate data redundancies with low computational complexity and communication and storage overhead. The efficiency and functionality of SED improves the usability in client-side. Finally, the comparing results show that the performance of our scheme is superior to that of the existing schemes

Federated learning (FL) enables multiple clients to jointly train a global learning model while keeping their training data locally, thereby protecting clients’ privacy. However, there still exist some security issues in FL, e.g., the honest-but-curious servers may mine privacy from clients’ model updates, and the malicious clients may launch poisoning attacks to disturb or break global model training. Moreover, most previous works focus on the security issues of FL in the presence of only honest-but-curious servers or only malicious clients. In this paper, we consider a stronger and more practical threat model in FL, where the honest-but-curious servers and malicious clients coexist, named as the non-fully trusted model. In the non-fully trusted FL, privacy protection schemes for honest-but-curious servers are executed to ensure that all model updates are indistinguishable, which makes malicious model updates difficult to detect. Toward this end, we present an Adaptive Privacy-Preserving FL (Ada-PPFL) scheme with Differential Privacy (DP) as the underlying technology, to simultaneously protect clients’ privacy and eliminate the adverse effects of malicious clients on model training. Specifically, we propose an adaptive DP strategy to achieve strong client-level privacy protection while minimizing the impact on the prediction accuracy of the global model. In addition, we introduce DPAD, an algorithm specifically designed to precisely detect malicious model updates, even in cases where the updates are protected by DP measures. Finally, the theoretical analysis and experimental results further illustrate that the proposed Ada-PPFL enables client-level privacy protection with 35% DP-noise savings, and maintains similar prediction accuracy to models without malicious attacks.

Frequent itemsets mining and association rules mining are among the top used algorithms in the area of data mining. Secure outsourcing of data mining tasks to the third-party cloud is an effective option for data owners. However, due to the untrust cloud and the distrust between data owners, the traditional algorithms which only work over plaintext should be re-considered to take security and privacy concerns into account. For example, each data owner may not be willing to disclose their own private data to others during the cooperative data mining process. The previous solutions are either not sufficiently secure or not efficient. Therefore, we propose aSecure andEfficientDataMiningOutsourcing (SecEDMO) scheme for secure outsourcing of frequent itemsets mining and association rules mining over the joint database (i.e., database aggregated from multiple data owners) in the paradigm of cloud computing. Based on our customized lightweight symmetric homomorphic encryption algorithm and a secure comparison algorithm, SecEDMO can ensure strong privacy protection and low data mining latency simultaneously. Moreover, the well-designed virtual transaction insertion algorithm can hide the information of the original database while still preserving the cloud’s ability to perform data mining over the obfuscated data. By evaluation of a numerical experiment and theoretical comparisons, the correctness, security, and efficiency of SecEDMO are confirmed.

Blockchain technology has attracted tremendous interest from both industry and academia. It is typically used to record a public history of transactions (e.g., payment/smart contract data), but storing nonpayment/contract data in transactions has been common. The ability to store data unrelated to payment/contract such as illicit data on blockchain may be abused for malicious purposes. For example, one may use blockchain to store the data related to child pornography and copyright violations, which are publicly visible and immutable. Moreover, an immutable blockchain is not suitable for all blockchain-based applications. So far, numerous redaction mechanisms for the mutable blockchain have been developed. In this paper, we aim at conducting a comprehensive survey that reviews and analyzes the state-of-the-art redaction mechanisms. We start by giving a general presentation of blockchain and summarize the typical methods of inserting data in blockchain. Next, we discuss the challenges of designing the redaction mechanism and propose a list of evaluation criteria. Then, redaction mechanisms of the existing mutable blockchains are systemically reviewed and analyzed based on our evaluation criteria. The analyses include algorithmic overviews, performance limitations, and security vulnerabilities. Finally, the comparisons and analyses provide new insights into these mechanisms. This survey will provide developers and researchers a comprehensive view and facilitate the design of future mutable blockchains.

Federated learning (FL) is a machine-learning setting, where multiple clients collaboratively train a model under the coordination of a central server. The clients' raw data are locally stored, and each client only uploads the trained weight to the server, which can mitigate the privacy risks from the centralized machine learning. However, most of the existing FL models focus on one-time learning without consideration for continuous learning. Continuous learning supports learning from streaming data continuously, so it can adapt to environmental changes and provide better real-time performance. In this article, we present a federated continuous learning scheme based on broad learning (FCL-BL) to support efficient and accurate federated continuous learning (FCL). In FCL-BL, we propose a weighted processing strategy to solve the catastrophic forgetting problem, so FCL-BL can handle continuous learning. Then, we develop a local-independent training solution to support fast and accurate training in FCL-BL. The proposed solution enables us to avoid using a time-consuming synchronous approach while addressing the inaccurate-training issue rooted in the previous asynchronous approach. Moreover, we introduce a batch-asynchronous approach and broad learning (BL) technique to guarantee the high efficiency of FCL-BL. Specifically, the batch-asynchronous approach reduces the number of client-server interaction rounds, and the BL technique supports incremental learning without retraining when learning newly produced data. Finally, theoretical analysis and experimental results further illustrate that FCL-BL is superior to the existing FL schemes in terms of efficiency and accuracy in FCL.

The real-time data generated from various smart devices will be released and shared for public to obtain numerous benefits. However, it will lead to individual privacy leakage because of data mining or analysis. Currently, many existing privacy protection models either fail to be directly applied in real-time data release or are unsatisfactory in terms of data utility and privacy protection. Toward this end, based on m-signature and fuzzy processing, an anonymous privacy protection model, named PMF, is proposed in this paper. Specifically, for the proposed model there are five advantages: 1) PMF defines m-signature for making each bucket with at least m different sensitive values instead of generating any counterfeit tuples, which can not only resist h-difference attack but also improve practical value; 2) the buckets satisfying m-signature are variable over time, and this flexibility of m-signature can improve the efficiency of dynamic update; 3) PMF can effectively insert, delete, and modify real-time data for release; 4) PMF applies fuzzy processing to handle the tuples in the candidate list, which strikes a good balance between the utility of released data and privacy protection; and 5) PMF adopts greedy heuristic algorithm to process update operations, which greatly reduces the information loss of released data. Furthermore, PMF is obviously more secure than the existing models in the real-time data release. Finally, the results of the comparison experiments on real-world and synthetic datasets illustrate that PMF is superior to the existing models in terms of data utility and efficiency.

Blockchain, as a secured, trusted, and decentralized architecture, is used to create secure and tamper-proof payment schemes, which can serve economies and societies without trusted parties. However, the transparency and traceability of blockchain severely restrict the anonymity of participants in the real world, which will cause participants' privacy leakage. Toward this end, in this paper, an anonymous off-blockchain micropayments scheme (AOM) is proposed for cryptocurrencies in the real world. In AOM, a payee receives micropayments from an “honest-but-curious” intermediary T by solving puzzles which are generated based on the standard RSA assumption. Meanwhile, T also receives micropayments from the payers by payee's solutions and T will randomly select the inputs of the merging transaction Tmer. In order to improve service efficiency of T and resist denial of service attack, one of the outputs of Tmeris paid for T as a service fee. Besides, AOM simultaneously ensures the correctness and fairness of transactions. Finally, from the analyses of property and security, AOM has strong unlinkability, ability for anti-attacks and unforgeability.