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.

Previous model inversion attacks in collaborative inference systems have only been demonstrated on simple models. They failed to produce convincing results on large models or large images. To improve the quality of the model inversion attack, two adaptive generative model inversion attacks are proposed. First, the distributional prior of the publicly available pre-trained Generative Adversarial Nets are used to guide the reconstruction process. Second, previous works only search the latent space of the pre-trained GAN. However, the pre-trained generative model and the target model are trained on different datasets. The distribution shifts of the datasets can lead to inevitable reconstruction errors. To handle the distribution shifts, the pre-trained generative model will be fine-tuned for target instances in the proposed adaptive generative model inversion attack. As a result, the proposed attacks can better handle the distribution shifts. And there is no need to train a separate image prior for each target model. The generators can be used to attack different models trained on different datasets within the same domain. Our extensive experiments demonstrate that the proposed attack achieves up to a 25.7% improvement in PSNR and a 26.4% improvement in SSIM compared to previous methods.

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.

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.

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.

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.

Even the artificial intelligence (AI) has been widely used and significantly changed our life, deploying the large AI models on resource limited edge devices directly is not appropriate. Thus, the model split inference is proposed to improve the performance of edge intelligence (EI), in which the AI model is divided into different sub-models and the resource-intensive sub-model is offloaded to edge server wirelessly for reducing resource requirements and inference latency. Unfortunately, with the sharp increasing of edge devices, the shortage of spectrum resource in edge network becomes seriously in recent years, which limits the performance improvement of EI. Refer to the NOMA-based edge computing (EC), integrating non-orthogonal multiple access (NOMA) technology with split inference in EI is attractive. However, the NOMA-based communication aspect and the influence of intermediate data transmission fail to be considered properly in model split inference of EI in previous works, and the sophistication in resource allocation caused by NOMA scheme makes it further complicated. Thus, the Effective Communication and Computing resource allocation algorithm is proposed in this paper for accelerating the split inference in NOMA-based EI, shorted as ECC. Specifically, the ECC takes the energy consumption and the inference latency into account to find the optimal model split strategy and resource allocation strategy (subchannel, transmission power, computing resource). Since the minimum inference delay and energy consumption cannot be satisfied simultaneously, the gradient descent (GD) based algorithm is adopted to find the optimal tradeoff between them. Moreover, the loop iteration GD approach (Li-GD) is developed to reduce the complexity of the GD algorithm caused by parameter discretization. The key idea of Li-GD is that: the initial value of the$i\mathrm {th}$layer’s GD procedure is selected from the optimal results of the former$(i-1)$layers’ GD procedure whose intermediate data size is the closest to$i\mathrm {th}$layer. Additionally, the properties of the proposed algorithms are investigated, including convergence, complexity, and approximation error. The experimental results demonstrate that the performance of ECC is much better than that of the previous studies.

Smart meters are deployed in the smart grid to achieve bidirectional communication, the control center can monitor, predict energy consumption data in real time, and adjust energy supply dynamically. Unfortunately, real-time data may divulge users’ private information. To protect the privacy of real-time data, data aggregation schemes have been proposed to assist the control center in adjusting the supply to meet users’ electricity demands without sacrificing data privacy. However, extreme weather events and potential attacks may damage the meters and change the structure of the smart grid dynamically, it is important to improve the reliability of the data aggregation scheme. Even if some schemes have improved reliability, but the scalability is poor, they are not suitable for the dynamically changing smart grid network structure. To meet this end, a resilient data aggregation scheme for the smart grid is proposed, which 1) offers better reliability by defending against malicious attacks, group management techniques are proposed, and meters can update their keys when the smart grid network structure changes; 2) affords higher scalability; and 3) enables the control center to make fine-grained adjustments. A prototype implementation shows that the proposed scheme is efficient enough for smart meters.

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.

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.”

In a wearable sensor-based deployment, sensors are placed over the patient to monitor their body health parameters. Continuous physiological information monitored by wearable sensors helps doctors have a better diagnostic and a suitable treatment. When doctors want to access the patient’s sensor data remotely via network, the patient will authenticate the identity of the doctor first, and then they will negotiate a key for further communication. Many lightweight schemes have been proposed to enable a mutual authentication and key establishment between the two parties with the help of a gateway node, but most of these schemes cannot enable identity confidentiality. Besides, the shared key is also known by the gateway, which means the patient’s sensor data could be leaked to the gateway. In PriAuth, identities are encrypted to guarantee confidentiality. Additionally, Elliptic Curve Diffie–Hellman (ECDH) key exchange protocol has been adopted to ensure the secrecy of the key, avoiding the gateway access to it. Besides, only hash and XOR computations are adopted because of the computability and power constraints of the wearable sensors. The proposed scheme has been validated by BAN logic and AVISPA, and the results show the scheme has been proven as secure.