Paging is anticipated to play a pivotal role in realizing the 6G vision for the IoE (Internet of Everything) and uRLLC (ultra-reliable low-latency communications) by enhancing service quality and improving resource efficiency. Nevertheless, as 6G architectures evolve toward large-scale, distributed architectures, existing rule-driven, centralized paging mechanisms face severe challenges, including inefficiencies due to massive mobile-device access, incompatibility with distributed network architectures, and significant privacy concerns. To address these challenges, this paper proposes a privacy-preserving and personalized federated intelligent paging mechanism for 6G Networks (6G-PFIP). 6G-PFIP introduces a novel intelligent decision model to enhance paging efficiency for large-scale user equipment (UE). A personalized hierarchical federated paging architecture (HFPA) is further constructed to improve architectural compatibility and privacy by decomposing the centralized paging process into distributed layers. HFPA effectively addresses the challenge of Non-IID user data in 6G by designing region-level and local-level personalization and fairness-aware aggregation strategies. Furthermore, HFPA integrates local differential privacy (LDP) on demand to perturb shared model parameters, thereby mitigating the risk of privacy leakage. Experiments on a 6G Telecom Dataset show that 6G-PFIP achieves significant signaling resource savings (71.33%) and delay reductions (34.78%), while rigorous analysis confirms its privacy guarantees.

SM9 is a national and also ISO/IEC standard concerning identity-based cryptosystem. It plays a significant role in numerous applications, such as medical collaboration services and e-government affairs. Motivated by the fact that the decryption in SM9 merely offers all-or-nothing access to the plaintext, Li et al. proposed a hierarchical identity-based broadcast inner product functional encryption from SM9. It supports inner product operations and one-to-many access control mechanism simultaneously. Nevertheless, their scheme suffers from low efficiency in practice. Its encryption/decryption complexity grows with the size of the broadcast set. To resolve this issue, we propose a wildcarded identity-based inner product functional encryption scheme based on SM9 (WIP-SM9), which achieves better efficiency and inner product functionality. Then we give a formal security proof to demonstrate that our WIP-SM9 scheme achieves ciphertext indistinguishability against selective pattern and vector, and chosen-plaintext attacks (IND-sPV-CPA) in the random oracle model. Eventually, the theoretical and experimental analyses for real-world scenarios indicate that WIP-SM9 outperforms existing related schemes in encryption and decryption algorithms.

Federated learning (FL) faces challenges in ensuring personalized model performance while maintaining strong privacy protection. Combined with knowledge distillation, federated learning enhanced by differential privacy has garnered increased attention. However, existing privacy-preserving federated distillation methods often apply a uniform privacy budget, neglecting the varying privacy needs across heterogeneous clients. In this letter, we propose PDP-FedKD, a Personalized Differential Privacy-based Federated Knowledge Distillation approach. PDP-FedKD allows clients to self-select their privacy budgets using a nonuniform sampling strategy and introduces tailored noise into distillation models across heterogeneous clients. Our approach ensures global model generalization and local model personalization while preserving the global privacy budget. We further analyze tight privacy accounting based on R & eacute;nyi differential privacy to optimize the trade-off between privacy and model accuracy. Experimental results in non-IID settings show that PDP-FedKD improves model performance by 4.06% over LDP-FedAKD with a uniform privacy budget and by 7.73% over DP-FedAvg without optimizing heterogeneity.

Identity-based encryption with equality test (IBEET) extends identity-based encryption to support the comparison between messages in two distinct ciphertexts associated with different identities. Nevertheless, nearly all of the previous IBEET schemes have the problem that once the trapdoor is sent to the cloud server, it becomes uncontrollable. Meanwhile, the only existing IBEET scheme coping with this problem, namely IBEET with datestamp-based authorization (IBEET-DBA), suffers from the following flaws. (1) A trapdoor merely expresses a single time interval, which is inexpressive. (2) It is inflexible due to only supporting limited types of authorization. (3) It is merely proven secure in the random oracle model, which has been pointed out that it may lead to security risks in practicality. Considering the aforementioned problems, we propose an IBEET with flexible time-based authorization (IBEET-FTBA) scheme, supporting wildcards and putting no constraint on authorization types. We prove our scheme secure against adaptive chosen-ciphertext attacks in the standard model. Subsequently, we give a detailed theoretical analysis of our IBEET-FTBA construction and the main existing IBEET ones. Furthermore, we implement ours and the IBEET-DBA construction as a benchmark, and conduct numerous experiments. Both results demonstrate that ours has better performance in the scenario of multiple time intervals.

Federated learning enables collaborative model training across distributed edge devices while strictly preserving data privacy. To facilitate practical deployment on resource-constrained edge devices, Spiking Neural Networks (SNNs) have emerged as a promising alternative to traditional Artificial Neural Networks (ANNs) due to their sparse computing mechanisms and high energy efficiency. However, jointly training ANNs and SNNs exposes a challenge of representational misalignment, which is intrinsically caused by differences in information representation, specifically the semantic gap between continuous real-valued activations in ANNs and discrete spatio-temporal spikes in SNNs. To overcome this barrier, we propose AS-FedBridge, a novel federated learning framework tailored for mixed ANN-SNN clients. AS-FedBridge features a lightweight Bridge equipped with a Pseudo-Spike Interface, which effectively projects continuous signals into a spike-compatible space to facilitate ANN-SNN alignment. Given the absence of existing mixed ANN-SNN federated frameworks, we establish a comprehensive benchmark to evaluate against multiple advanced heterogeneous FL methods. Our empirical analysis demonstrates a positive correlation between the degree of ANN-SNN alignment and the collaborative FL performance. Across four datasets, AS-FedBridge consistently demonstrates advanced accuracy while mitigating extreme scale, architecture, and client heterogeneity challenge. Furthermore, our framework enables a highly controllable trade-off between model performance and resource efficiency. AS-FedBridge accomplishes these robust performance gains while introducing only marginal computational overhead, establishing a robust and practical foundation for mixed ANN-SNN federated learning systems.

MEC is set to play a pivotal role in 6G, supporting the Internet of Everything. 3GPP has proposed user mobility analysis and prediction methods to manage vast user data. However, by bringing services closer to the network edge, MEC improves user access efficiency and exposes user predictions, including location data, to greater privacy risks and potential malicious attacks. Additionally, conducting experiments on large-scale user populations increases communication and training costs. To address these challenges, we propose the Digital-Twin-Enabled Privacy-Preserving Federated User Prediction Framework (6G-DTFP) for 6G MEC. This architecture incorporates a personalized model, enhances training efficiency, and strengthens privacy protection using differential privacy mechanisms. By leveraging Digital Twin technology, it maps real user entities to the virtual environment, improving insights into user characteristics and optimizing resource utilization. Experimental results show that this framework offers reliable user prediction and aligns with the sustainable development goals of 6G networks.

Recently, redactable blockchains have attracted attention owing to enabling the contents of blocks to be re-written. The existing redactable blockchain solutions can be classified as two categories, the centralized one and decentralized one. In centralized solutions, a single blockchain node possessing the trapdoor conducts redaction operations. However, they suffer from the issue of single point of failure. In decentralized solutions, redaction operations are performed by numerous blockchain nodes cooperatively. But there also exists the issue of inefficiency or requiring a trusted party in these solutions. Subsequently, Jia et al. proposed a redactable blockchain solution from a decentralized chameleon hash function (DCH) they designed, which supports the threshold redaction, traceability and consistency check. Nevertheless, after carefully analyzing their solution, we find that it fails to achieve the security they claimed by presenting a concrete attack. To resolve this security issue, we propose a novel chameleon hash function scheme that achieves strong collision-resistant security while maintaining simple and efficient as the building block. Based on it, we then present an improved DCH scheme with sufficient security, so that the redactable blockchain from it can resist the presented attack. Theoretical and experimental analyses demonstrate that improved DCH achieves efficiency comparable to DCH.

Online/offline identity-based signature (OO-IBS) is a versatile cryptographic tool to provide the message authentication and integrity in mobile devices, since it lightens the computational burden after the signer receiving the message and eliminates the overhead of certificate management. It has several valuable applications, for instance, wireless sensor networks. Identity-based chameleon hash (IB-CH), as an alternative building block to construct OO-IBS, has been explored in numerous literatures. Nevertheless, there still exist two major issues. (1) Nearly all of the previous IB-CH schemes with weak collision-resistance (W-CollRes) are with random oracles, which may lead to security risks in practicality. The only IB-CH scheme in the standard model suffers from the large size of public parameters and inefficient setup process. (2) The only IB-CH scheme without key exposure also relies on random oracles. In this paper, we propose two novel IB-CH schemes in the standard model. The first scheme is adaptive identity, W-CollRes secure and efficient, significantly reducing the computation costs of all algorithms and the size of public parameters compared with the existing scheme in the standard model. The second scheme is the first IB-CH achieving key exposure freeness without random oracles. Both theoretical and experimental analyses demonstrate the good performance of our proposed schemes. Furthermore, we apply our schemes to optimizing the existing generic OO-IBS construction. The optimized generic constructions reduce computational overhead by 50.0% in the online phase and enable the hash value/signature tuple generated in the offline phase to be reusable, respectively.

The use of cryptocurrencies has become an emerging and popular way of trading as they gain legitimacy. To address the issue of privacy leakage, some techniques to hide transaction amounts have been proposed such as the MimbleWimble protocol. However, these privacy enhancement schemes basically apply to oneto-one tradings between one payer and one payee, resulting in cryptocurrencies not being used in broader scenarios such as more than one payer or payee (referred to as multi-party transactions in this paper). In this work, we propose a new privacy-preserving decentralized multi-party payment (PDMP) scheme that ensures the transaction amounts in multi-party transactions remain confidential to other parties, and define the ideal functionality for it which captures the privacy and security properties in cryptocurrencies. Then we instantiate a lattice-based PDMP protocol in a hybrid model which can universally composable (UC) securely realize the functionality with a simulation-based security proof. We construct a lattice-based verifiable multi-secret sharing scheme and a lattice-based multi-prover non-interactive zero-knowledge argument to support the protocol, both of which enjoy the security in the future quantum computer era. At last, we have carried out experimental implementation of the scheme to prove its feasibility.

Multiparty private set intersection (MPSI) allows multiple participants to compute the intersection of their locally owned data sets without revealing them. MPSI protocols can be categorized based on the network topology of nodes, with the star, mesh, and ring topologies being the primary types, respectively. Given that star and mesh topologies dominate current implementations, most existing MPSI protocols are based on these two topologies. However, star-topology MPSI protocols suffer from high leader node load, while mesh topology protocols suffer from high communication complexity and overhead. In this paper, we first propose a multi-point sequential oblivious pseudorandom function (MP-SOPRF) in a multi-party setting. Based on MP-SOPRF, we then develop an MPSI protocol with a ring topology, addressing the challenges of communication and computational overhead in existing protocols. We prove that our MPSI protocol is semi-honest secure under the Hamming correlation robustness assumption. Our experiments demonstrate that our MPSI protocol outperforms state-of-the-art protocols, achieving a reduction of 74.8 communication and a 6

Federated learning (FL) as a privacy-preserving technology enables multiple clients to collaboratively train models on decentralized data. However, transmitting model parameters between local clients and the central server can potentially result in information leakage. Differentially private federated learning (DPFL) has emerged as a promising solution to enhance privacy. Nevertheless, existing DPFL schemes suffer from two issues: (i) most schemes that aim to achieve desired model accuracy may incur a high privacy budget. (ii) several schemes that consider the trade-off between privacy and accuracy by utilizing linear clipping bound may distort numerous model parameters. In this paper, we first propose FDP-FL, a flexible differential privacy approach for FL. FDP-FL introduces a novel series sum privacy budget allocation instead of uniform allocation and enables adaptive and nonlinear noise scale decay. In this way, a tight bound for cumulative privacy loss can be achieved while optimizing model accuracy. Then in order to mitigate gradient leakages caused by honest-but-curious clients and server, we further design client-level FDP-FL and record-level FDP-FL, respectively. Experimental results demonstrate that our FDP-FL improves model accuracy by $\sim $13.3% compared with the basic DP-FL under a fixed privacy budget and outperforms existing trade-off schemes with the same hyperparameter setting.

Federated learning is widely used in medical applications for training global models without needing local data access. However, varying computational capabilities and network architectures (system heterogeneity), across clients pose significant challenges in effectively aggregating information from non-independently and identically distributed (non-IID) data. Current federated learning methods using knowledge distillation require public datasets, raising privacy and data collection issues. Additionally, these datasets require additional local computing and storage resources, which is a burden for medical institutions with limited hardware conditions. In this paper, we introduce a novel federated learning paradigm, named Model Heterogeneous personalized Federated Learning via Injection and Distillation (MH-pFLID). Our framework leverages a lightweight messenger model that carries concentrated information to collect the information from each client. We also develop a set of receiver and transmitter modules to receive and send information from the messenger model, so that the information could be injected and distilled with efficiency.

In medical image segmentation, personalized cross-silo federated learning (FL) is becoming popular for utilizing varied data across healthcare settings to overcome data scarcity and privacy concerns. However, existing methods often suffer from client drift, leading to inconsistent performance and delayed training. We propose a new framework, Personalized Federated Learning via Feature Enhancement (pFLFE), designed to mitigate these challenges. pFLFE consists of two main stages: feature enhancement and supervised learning. The first stage improves differentiation between foreground and background features, and the second uses these enhanced features for learning from segmentation masks. We also design an alternative training approach that requires fewer communication rounds without compromising segmentation quality, even with limited communication resources. Through experiments on three medical segmentation tasks, we demonstrate that pFLFE outperforms the state-of-the-art methods.

Representation learning frameworks in unlabeled time series have been proposed for medical signal processing. Despite the numerous excellent progresses have been made in previous works, we observe the representation extracted for the time series still does not generalize well. In this paper, we present a Time series (medical signal) Representation Learning framework via Spectrogram (TRLS) to get more informative representations. We transform the input time-domain medical signals into spectrograms and design a time-frequency encoder named Time Frequency RNN (TFRNN) to capture more robust multi-scale representations from the augmented spectrograms. Our TRLS takes spectrogram as input with two types of different data augmentations and maximizes the similarity between positive ones, which effectively circumvents the problem of designing negative samples. Our evaluation of four real-world medical signal datasets focusing on medical signal classification shows that TRLS is superior to the existing frameworks.

In the evolving application of medical artificial intelligence, federated learning is notable for its ability to protect training data privacy. Federated learning facilitates collaborative model development without the need to share local data from healthcare institutions. Yet, the statistical and system heterogeneity among these institutions poses substantial challenges, which affects the effectiveness of federated learning and hampers the exchange of information between clients. To address these issues, we introduce a novel approach, MH-pFLGB, which employs a global bypass strategy to mitigate the reliance on public datasets and navigate the complexities of non-IID data distributions. Our method enhances traditional federated learning by integrating a global bypass model, which would share the information among the clients, but also serves as part of the network to enhance the performance on each client. Additionally, MH-pFLGB provides a feature fusion module to better combine the local and global features. We validate MH-pFLGB's effectiveness and adaptability through extensive testing on different medical tasks, demonstrating superior performance compared to existing state-of-the-art methods.

Nearly all of the previous attribute-based proxy re-encryption (ABPRE) schemes cannot support keyword search and keyword updating without the aid of private key generator (PKG) simultaneously. To resolve this problem, recently in IEEE Transactions on Dependable and Secure Computing (doi: 10.1109/TDSC.2020.2963978), Ge et al. proposed a ciphertext-policy ABPRE scheme with keyword search, dubbed CPAB-KSDS, which supports keyword updating without communicating with PKG. It also achieves indistinguishability against chosen-ciphertext attack (IND-CCA) security and indistinguishability against chosen-keyword attack (INDCKA) security in the random oracle model. In this paper, we carefully analyze the security of Ge et al.’s CPAB-KSDS scheme and find that they did not give a correct reduction from IND-CKA security of theirs to the underlying cryptographic assumption. Furthermore, we also give a concrete attack on IND-CKA security of the CPAB-KSDS scheme. Therefore, it fails to achieve IND-CKA security they claimed, which is an essential security requirement for the encryption scheme with keyword search.

The rise of new industries, such as the Internet of Things and Smart Healthcare, has brought many cross-cloud business opportunities for cloud computing and posed new challenges to the cloud security. Traditionally, security can be assessed by compliance checking when selecting cloud services. However, when facing cross-cloud security requirements, even if passing the compliance checking, it cannot prove that different clouds have the same security level since they pass different standards. Therefore, security equivalence assessment of different security standards is a fundamental issue. In order to solve the issue automatically, we first transform it into the problem of mapping between control items with respect to different standards. Then, we define three tasks to work out the mapping problem: a task for mapping searching and two for new mapping establishing. Next, we collect, organize, and expand a dataset of mappings between control items containing 21 standards and more than 100,000 pieces of mapping data. Subsequently, we experiment with four well-known models for each task to test their performance on the dataset of mappings: TF-IDF, Word2vec, BERT, and GPT-Neo. Experimental results indicate that the current models can perform very well on the first two tasks but need to be better on the last task.

Federated learning (FL) allows different participants to collaborate on model training without transmitting raw data, thereby protecting user data privacy. However, FL faces a series of security and privacy issues (e.g. the leakage of raw data from publicly shared parameters). Several privacy protection technologies, such as homomorphic encryption, differential privacy and functional encryption, are introduced for privacy enhancement in FL. Among them, the FL frameworks based on functional encryption better balance security and performance, thus receiving increasing attention. The previous FL frameworks based on functional encryption suffer from several security issues, including attacks by combining multiple rounds of ciphertexts and keys, and leakage of global parameters to the central server. To tackle these issues, we propose a novel multi-input functional proxy re-encryption (MI-FPRE) scheme and further design a new FL framework with better privacy based on MI-FPRE. Our framework allows a semi-trusted central server to aggregate the parameters without knowing the intermediate parameters and the result of aggregation, thus achieves better privacy in FL training. The experimental results indicate that our framework achieves less communication overhead and higher computational efficiency without losing accuracy.

The rapid identification and accurate diagnosis of breast cancer, known as the killer of women, have become greatly significant for those patients. Numerous breast cancer histopathological image classification methods have been proposed. But they still suffer from two problems. (1) These methods can only hand high-resolution (HR) images. However, the low-resolution (LR) images are often collected by the digital slide scanner with limited hardware conditions. Compared with HR images, LR images often lose some key features like texture, which deeply affects the accuracy of diagnosis. (2) The existing methods have fixed receptive fields, so they can not extract and fuse multi-scale features well for images with different magnification factors. To fill these gaps, we present a Single Histopathological Image Super-Resolution Classification network (SHISRCNet), which consists of two modules: Super-Resolution (SR) and Classification (CF) modules. SR module reconstructs LR images into SR ones. CF module extracts and fuses the multi-scale features of SR images for classification. In the training stage, we introduce HR images into the CF module to enhance SHISRCNet's performance. Finally, through the joint training of these two modules, super-resolution and classified of LR images are integrated into our model. The experimental results demonstrate that the effects of our method are close to the SOTA methods with taking HR images as inputs.

Computer-aided diagnosis plays an increasingly important role in modern medical activities, relying largely on the deployment of medical machine learning models. Protecting the security of model parameters is crucial for model providers. However, the current schemes for protecting model parameters are mostly interactive. This interactive nature makes it difficult to support offline deployment of models and flexible authorization of prediction results, thus hindering the widespread application of computer-aided diagnosis. To address these limitations, we propose a new computer-aided medical diagnosis framework by designing a new identity-based inner product functional proxy re-encryption (IB-IPFPRE) scheme. Our framework supports private deployment of medical diagnostic models without compromising model parameters. It also enables access control of prediction results based on user identity. Compared to existing privacy-preserving prediction techniques, our framework significantly reduces communication overhead and does not require the model owner to be online in real-time. Furthermore, our scheme enables flexible delegation of prediction results, allowing users to authorize the sharing of prediction results with other entities as needed. We conducted extensive experiments for logistic regression on three medical datasets. The experiments demonstrate that our scheme achieved 40% to 7× performance improvement in LAN environment and 13× to 15× improvement in WAN environment, and did not require any communication overhead during the privacy preserving prediction phase.