Decentralized storage auditing approaches are designed to ensure data security in dishonest decentralized storage providers. However, the need for data updates introduces new challenges to the design of decentralized storage auditing approaches. Existing approaches can support dynamic auditing for updated files. Unfortunately, they can only deal with block-level updating, which is counter-intuitive and requires conversion from semantic changes to binary changes. Furthermore, existing dynamic auditing approaches require the recalculation of auxiliary auditing information (e.g., auditing authenticators) in data owners, which imposes unnecessary additional burdens on data owners, particularly those with constrained resources in decentralized storage environments. In this paper, we focus on image files and propose iAudit, an efficient pixel-level dynamic image auditing approach in decentralized storage. We first design a novel image authenticator with image pixels for efficient dynamic auditing, which combines convolution operations and polynomial commitment in authenticator construction. Additionally, we build an owner-free dynamic mechanism in dynamic decentralized storage auditing approach by utilizing zero-knowledge proof techniques. In this way, the dynamic operation overheads incurred by auditing can be completely eliminated from the data owners. A prototype of iAudit is implemented, and extensive experimental results demonstrate that iAudit outperforms state-of-the-art works, achieving over a 210× speedup for data owner in dynamic update phase.

Federated fine-tuning efficiently adapts large pre-trained models to new tasks by using additional data while minimizing re-training costs. This approach enhances data privacy and reduces computational demands but relies on a central server, often cloud-based, which is vulnerable to adversarial attacks that can compromise the aggregation process. We propose${\sf FLAGuard}$, a novel and efficient verification scheme that specifically addresses these challenges in federated Low-Rank Adaptation (LoRA) settings.${\sf FLAGuard}$is the first to introduce a two-stage verification process specifically designed for LoRA-based aggregation. In the first stage, the scheme independently verifies the correctness of the aggregated$A$and$B$matrices. In the second stage, it verifies the multiplication result of the aggregated$A$and$B$matrices, ensuring the correctness of the final LoRA parameters. Additionally, we introduce the Iterative Gradient Sampling and Convolutional Compression (IGSCC) technique, which combines probabilistic sampling with convolutional operations to efficiently reduce the dimensionality of gradient matrices. This enables secure verification without sacrificing model performance. Our comprehensive security analysis of${\sf FLAGuard}$further establishes its reliability in federated learning environments. Extensive experimental results demonstrate that${\sf FLAGuard}$achieves over a$100\times$speedup in the aggregation verification phase and reduces communication overhead by more than 50% compared to state-of-the-art methods.

In recent years, the focus on data privacy and security has intensified, with Secure Multi-party Computation (MPC) providing privacy protection for data and models at the cost of increased computational demands. While existing studies emphasize the computational requirements of non-linear inference, our findings reveal that linear computations can also significantly impact model speed, especially in resourceconstrained environments. In this work, we introduce MPCFLC, an optimization framework for secure inference models. Our innovative two-stage distillation process, which integrates matrix decomposition with non-linear substitution, achieves a$2.52 \times$speedup in inference with negligible performance degradation. Furthermore, our specially crafted distillation method enhances distillation speed by$1.3 \times$, further minimizing accuracy loss. Experiments conducted on the GLUE dataset validate the effectiveness of our proposed approach.

ABSTRACT Federated learning (FL) for medical image segmentation poses critical challenges, including non‐IID data distributions, limited access to labelled annotations, and stringent privacy constraints across institutions. To address these, we propose FUSION (Federated Unified Semi‐Supervised Optimisation Network), a novel dual‐path training framework that integrates both Federated Labelled Data Learning (FLDL) and Federated Unlabelled Data Training (FUDT). Central to FUSION is a two‐stage pseudo‐label refinement strategy designed to ensure robustness under real‐world federated constraints. First, synthetic label denoising is performed using Monte Carlo dropout‐based uncertainty estimation, enabling clients to identify and exclude low‐confidence predictions. Second, prototype‐based correction is applied to further refine pseudo‐labels by aligning them with class‐specific feature centroids, mitigating errors caused by domain shifts and inter‐client variability. These refined labels are used for localised training on unlabelled clients, while a dynamic aggregation scheme modulated by a reliability‐based hyperparameter μ adjusts the influence of labelled versus unlabelled clients during global model updates. This tightly coupled interaction between pseudo‐label quality and federated optimisation ensures stability, accelerates convergence, and enhances generalisation across heterogeneous clients. FUSION is evaluated on three diverse datasets: TCGA‐LGG (brain MRI), Kvasir‐SEG (colonoscopy), and UDIAT (ultrasound) and consistently outperforms state‐of‐the‐art FL models in Dice, IoU, HD95, and ASD metrics. Results confirm the critical role of synthetic label refinement in enhancing segmentation accuracy, boundary precision, and model scalability. FUSION provides a technically grounded, privacy‐preserving, and label‐efficient solution for real‐world multi‐institutional medical image segmentation tasks.

The rapid development of Industrial IoT (IIoT) has caused the explosion of industrial data, which opens up promising possibilities for data analysis in IIoT networks. Due to the limitation of computation and storage capacity, IIoT devices choose to outsource the collected data to remote cloud servers. Unfortunately, the cloud storage service is not as reliable as it claims, whilst the loss of physical control over the cloud data makes it a significant challenge in ensuring the integrity of the data. Existing schemes are designed to check the data integrity in the cloud. However, it is still an open problem since IIoT devices have to devote lots of computation resources in existing schemes, which are especially not friendly to resource-constrained IIoT devices. In this paper, we propose an efficient storage verification approach for cloud-assisted industrial IoT platform by adopting a homomorphic hash function combined with polynomial commitment. The proposed approach can efficiently generate verification tags and verify the integrity of data in the industrial cloud platform for IIoT devices. Moreover, the proposed scheme can be extended to support privacy-enhanced verification and dynamic updates. We prove the security of the proposed approach under the random oracle model. Extensive experiments demonstrate the superior performance of our approach for resource-constrained devices in comparison with the state-of-the-art.

Federated Learning (FL) represents a distributed machine learning approach, enabling the joint training of a global model through the aggregation of gradients from participating clients without necessitating the exchange of raw data. Prior research has explored methods for verifying the correctness of aggregation in this context and mitigating the overhead associated with the verification process. Nonetheless, the advent of Large Language Models (LLMs), with their parameters numbering in the billions, presents ongoing challenges in devising efficient verification mechanisms in FL for large models. In this paper, we propose an innovative Efficient Verifiable Federated Learning scheme${\sf LaVFL}$, which focusing on addressing the verification challenges incurred by LLM. Specifically, we propose an efficient layer-by-layer verification approach for LLMs by designing a Convolution Gradient Compression (CGC) method without compromising model accuracy. Additionally, to minimize computational and communication overheads, we propose an efficient verification strategy PGS, namely, a Probabilistic Gradient Sampling strategy, which aims to reduce the gradient dimensions for each round of verification while ensuring a high probability of comprehensive verification. We implement a prototype of${\sf LaVFL}$, and extensive experimental results demonstrate that${\sf LaVFL}$achieves over a$300 \times$speedup in the aggregation verification phase and reduces communication overheads by more than 75%, compared to VeriFL under the same experimental setup.

The rapid development of the Industrial Internet of Things (IIoT) has led to an explosion of industrial data. Due to computing and storage capacity limitations, IIoT devices often outsource the collected data to remote cloud servers. Unfortunately, cloud storage and cloud sharing services are not as reliable as they claim to be. Existing schemes aim to check data integrity in the cloud through cloud auditing. However, they suffer from a number of security and privacy vulnerabilities. The challenge of designing a secure storage auditing framework for industrial IoT comes from two aspects: 1) lack of physical protection of data owner IIoT devices; 2) privacy issues due to auditing of sensitive shared data. Inspired by the aforementioned challenges, we design the secure storage audit framework to support flexible cloud data sharing in IIoT: S2A-P2FS. The first contribution in our work is the Polynomial Prefix Message Authentication Code(P2MAC) design. We design an innovative P2MAC data structure as a label, which can simultaneously achieve efficient data verification in cloud data storage and privacy protection in flexible cloud data sharing for cloud auditing. The second contribution is the design of a unique Physical Unclonable Function(PUF) for IIoT. Harsh industrial conditions hinder the stable operation of PUFs. To protect the trustness of IIoT data owners, we propose a robust PUF-based physical protection mechanism for IIoT devices. The key point is that the required key is not stored in the memory of IIoT but hidden within its physical structure. A security analysis was conducted to demonstrate the robustness of S2A-P2FS against known vulnerabilities. A prototype was implemented in a real-world IIoT scenario. Experimental results indicate that, compared to state-of-the-art schemes, S2A-P2FS achieves over a 3x speedup in computational time and requires only 67.5% of the communication cost.

As many countries have promulgated laws to protect users’ data privacy, how to legally use users’ data has become a hot topic. With the emergence of federated learning (FL) (also known as collaborative learning), multiple participants can create a common, robust, and secure machine learning model while addressing key issues in data sharing, such as privacy, security, accessibility, etc. Unfortunately, existing research shows that FL is not as secure as it claims, gradient leakage and the correctness of aggregation results are still key problems. Recently, some scholars try to address these security problems in FL by cryptography and verification techniques. However, there are some issues in this scheme that remain unsolved. First, some solutions cannot guarantee the correctness of the aggregation results. Second, existing state-of-the-art FL schemes have a costly computational and communication overhead. In this article, we propose SVFLC, a secure and verifiable FL scheme with chain aggregation to solve these problems. We first design a privacy-preserving method that can solve the problem of gradient leakage and defend against collusion attacks by semi-honest users. Then, we create a verifiable method based on a homomorphic hash function, which can ensure the correctness of the weighted aggregation results. Besides, the SVFLC can also track users who encounter calculation errors during the aggregation process. Additionally, the extensive experiment results on real-world data sets demonstrate that the SVFLC is efficient, compared with other solutions.

Industrial Internet platforms (IIP) can provide many intelligent services based on the industrial big data stored in clouds. However, the vulnerability of cloud storage can cause data corruption, demanding verifying its integrity. Unfortunately, existing cloud storage verification approaches cannot be directly applied to IIP, since they pose heavy computational burdens on the edge side. In this work, we propose an efficient and dynamic storage verification scheme Edasvic for cloud storage in the IIP. We adopt the polynomial commitment to build an efficient homomorphic authenticator, and further design an authenticator accumulator, which can be efficiently generated with limited computational overheads. In addition, we integrate the dynamic information into the authenticator accumulator to support data dynamics. The security of Edasvic is analyzed under the random oracle model. We conduct extensive experiments to evaluate the performance of Edasvic and compare it with the state-of-the-art approaches. Experimental results affirm that Edasvic is superior to existing solutions in terms of computational efficiency.

In the era emphasizing the privacy of personal data, verifiable federated learning has garnered significant attention as a machine learning approach to safeguard user privacy while simultaneously validating aggregated result. However, there are some unresolved issues when deploying verifiable federated learning in edge computing. Due to the constraint resources, edge computing demands cost saving measurements in model training such as model pruning. Unfortunately, there is currently no protocol capable of enabling users to verify pruning results. Therefore, in this paper, we introduce PrVFL, a verifiable federated learning framework that supports model pruning verification and heterogeneous edge computing. In this scheme, we innovatively utilize zero-knowledge range proof protocol to achieve pruning result verification. Additionally, we first propose a heterogeneous delayed verification scheme supporting the validation of aggregated result for pruned heterogeneous edge models. Addressing the prevalent scenario of performance-heterogeneous edge clients, our scheme empowers each edge user to autonomously choose the desired pruning ratio for each training round based on their specific performance. By employing a global residual model, we ensure that every parameter has an opportunity for training. The extensive experimental results demonstrate the practical performance of our proposed scheme.

The rapid development of Industrial IoT (IIoT) opens up promising possibilities for data analysis and machine learning in IIoT networks. As a distributed paradigm, federated learning (FL) allows numerous IIoT devices to collaboratively train a global model without collecting their local data together in central servers. Unfortunately, a centralized server used to aggregate local gradients can be compromised and forge the result, which incurs the need for aggregation verification. Several approaches focusing on verifying the correctness of aggregation have been proposed. However, it is still an open problem since devices have to devote more computation resources for verification, which are especially not friendly to resource-constrained IIoT devices. Furthermore, verifying weighted aggregation has not been supported in existing approaches. In this paper, we propose an efficient verifiable federated learning approach for IIoT networks, which verifies the aggregation of gradients and requires lowest burden on IIoT devices by introducing zero-knowledge proof techniques. Moreover, our design supports weighted aggregation verification to validate the aggregation of weighted gradients in the cloud server. By comparing the proposed approach with the state-of-the-art schemes including VerifyNet and VeriFL, we demonstrate the superior performance of our approach for resource-constrained devices, which minimizes the computational overheads of the IIoT devices.

Blockchain technology, known for its decentralized and immutable nature, serves as the foundation for various applications. As a prominent application of blockchain, decentralized storage is powered by blockchain technology and is expected to provide a reliable and cost-effective alternative to traditional centralized storage. A major challenge in blockchain-powered decentralized storage is how to guarantee the quality of storage services in decentralized storage nodes (DSNs). Storage auditing can ensure the integrity and security of the stored data. Unfortunately, it incurs additional computational costs for data owners and extra storage overheads for DSNs, which thereby cannot be directly applied to decentralized storage networks consisting of nodes with various computation and storage capacity. In this article, we overcome these problems and minimize additional burdens in storage auditing. We propose EDCOMA, a computation and storage efficient auditing scheme for blockchain-based decentralized storage, in which a double compression method is designed to compress data authenticators using both data and polynomial commitment. To prevent replay attacks on double compression launched by DSNs, we introduce zero knowledge proof and design a compression arithmetic circuit to guarantee the execution of compression operations in DSNs. We analyze the security of EDCOMA under the random oracle model and conduct extensive experiments to evaluate the performance of EDCOMA. Experimental results affirm that EDCOMA outperforms state-of-the-art approaches in both computational and storage efficiency.

The deep integration of informatization and industrialization has resulted in an increasingly close connection between supervisory control and data acquisition (SCADA) systems and the Internet. The boundaries of the SCADA system are monitored by industrial smart sensors, which only have limited security protection and face severe security threats. One major threat is that smart sensors are vulnerable to physical attacks because they are often installed in unsafe areas far from plant protection. Under this attack, the data of sensors can be easily tampered with. Moreover, since sensors are resource-constrained physical devices, complex and expensive encryption algorithms are not applicable. In this paper, we design a lightweight industrial smart sensor data stream integrity verification scheme based on physical unclonable function (PUF) for industrial IoT, which can protect the physical security of sensors and the integrity of data streams to ensure the secure transmission of industrial smart sensor data streams. We utilize PUF, fuzzy extractor and bit selection algorithm to generate stable PUF responses. A malicious attacker cannot extract the key information through physical attack. In addition, we design a lightweight integrity verification algorithm with efficient key updating based on lightweight cryptographic primitives, making it suitable for resource-constrained physical devices. We perform the security analysis to demonstrate the security of the scheme to known security vulnerabilities. We implement the proposed scheme and evaluate the performance of our scheme with extensive experiments. The experimental results show the scheme is efficient and superior to existing schemes in computational and communication efficiency.

The rapid growth of various vehicular apps such as automotive navigation and in-car entertainment has brought the explosion of vehicular data. Such a growth has given rise to a huge challenge of maintaining the quality of cloud storage services for the whole period of storage in vehicular networks. As a result, poor quality of services easily causes data corruption problems and thereby threats vehicular data integrity. Blockchain, a tamper-proofing technique, is considered a promising approach for mitigating data integrity risks in cloud storage. However, existing blockchain-based schemes for auditing long-term cloud data integrity suffer from poor communication performance in a vehicular network. In this study, a blockchain-based offline auditing scheme for cloud storage in the vehicular network is proposed to improve auditing performance. Inspired by the data structure of blockchain, we design an evidence chain to achieve offline auditing, which allows the cloud to spontaneously generate data integrity evidence without communicating with auditors during the evidence generation phase. Furthermore, we extend our scheme to support public and automatic validation based on the smart contract. We prove the security of the proposed scheme under the random oracle model and further provide the performance evaluation by comparing with the state-of-the-art approaches.

With the increase of data centers, the number of disks also grows rapidly. Therefore, the prediction of disk failures has become an important task for both academia and industry. Existing prediction schemes predict disk failure in the short prediction horizon or with a short time window. However, these schemes cannot achieve ideal performance for a long prediction horizon with a long time window. In this paper, we proposed a deep learning method that can effectively solve the above problems. We refine the Self-Monitoring, Analysis and Reporting Technology (SMART) attributes by using information entropy to select the most related attributes for prediction. Moreover, we proposed the Multiple Channel Convolutional Neural Network based LSTM (MCCNN-LSTM) model to predict whether disk failures will occur in a given disk in next few days. We further evaluate the MCCNN-LSTM model by comparing it with the state-of-the-art works. Extensive experiments show that our model can improve FDR (Fault Detection Rate) to 99.8% and reduce FAR (False Alarm Rate) to 0.2%.

In the smart grid, meters report their real-time electricity consumption data to a utility supplier, and the utility supplier can adjust its supply accordingly. However, adversaries can infer users' privacy behaviors based on publicly transferred real-time electricity consumption data. Data aggregation schemes protect users' privacy from being leaked. We find two major problems are unsolved: 1) meter failure problem and 2) dynamic membership problem. To solve these problems, we designed a dynamic membership group-based multiple-data aggregation scheme. First, a group-based key establishment scheme is proposed, meters are divided into groups, meters in a group build keys to encrypt their data, the meter failure problem is alleviated. If one group has broken meters, the other groups will not be affected. Second, the dynamic join, dynamic leave, and meter replacement techniques are proposed, and the dynamic membership is achieved by allowing meters to update their keys. The simulation results show a meter's computation cost and communication cost are the minima among the related works, which makes the proposed scheme more suitable for the IoT scenario. Besides, we designed a data encoding method and a data retrieve method, we designed two attacks: 1) “bilinear map pairing attack” and 2) “zero attack.”

Summary As an essential component of cloud computing, cloud storage provides flexible data storage services for individuals and organizations. By storing multiple replicas of data in servers, cloud storage providers (CSPs) can improve availability and stability of the cloud storage service. To ensure that all data replicas of a cloud user are intact and completely stored in the CSP, many multi‐replica cloud auditing schemes have been proposed. However, such schemes are predominantly based on the public key infrastructure (PKI), which incurs complex certificate management. In addition, existing schemes do not consider support for sector‐level dynamic auditing. In this paper, we propose a fine‐grained dynamic multi‐replica data auditing scheme that has the following features: (1) it uses ID‐based cryptography to eliminate the cost of certificate management, (2) it supports efficient sector‐level dynamic operations on cloud user data, and (3) it optimizes the challenge algorithm to reduce the computational cost of the third party auditor (TPA). We show that the proposed scheme is provably secure based on a random oracle model. The performance analysis and experiments show the efficiency of the proposed scheme.