Intrusion detection is a crucial method for addressing the security risks of the Industrial Internet of Things (IIoT). However, acquiring substantial and high-quality training data can be challenging for centralized schemes. While federated learning has shown great application prospects as a secure distributed solution, it also encounters the problems of heterogeneous and imbalanced data in real-world production environments. In this article, we propose a personalized federated learning scheme based on Shannon entropy metric (PFLSE), aimed at providing a high-accuracy customized detection model for local organizations. This scheme introduces Shannon entropy into the aggregation mechanism, allowing the edge agent model, which contains richer global information, to carry greater weight in the aggregation process. In the local training process, a two-stage training strategy based on the concept of personalized layer is firstly applied to strengthen the global features and local personalized representations. Secondly, considering the differential balance degree between various edge agent data, a Shannon entropy based dynamic loss function (SDL) is proposed, which combines focal loss and cross-entropy loss, to improve training stability and alleviate the difficulty of training on imbalanced data. Finally, a comprehensive experiment simulating a real-world environment shows that PFLSE exhibits reliable intrusion detection performance across metrics such as accuracy, precision, andF1-Score. Furthermore, it outperforms other methods in the scenarios involving non-independent and identically distributed (non-IID) data.

While Federated Learning is intended to safeguard data privacy, it is confronted with the problem of gradient leakage, which empowers attackers to execute gradient inversion attacks and retrieve the original data through the shared gradient information. Existing gradient inversion attack methods can achieve good results when handling small batches of low-resolution images. However, when dealing with large batches of high-resolution images, problems such as gradient ambiguity and model instability will occur, resulting in a significant decrease in the recovery performance. We propose a novel Image-correction-penalty based Over-parameterized Regression Network (IORN). IORN breaks through the limitations of existing methods with its unique design. The Adaptive Over-parameterized Network in IORN can dynamically adjust its structure, thereby enhancing the network’s ability to capture complex data distributions. This enables it to better handle the complexity of large batches of high-resolution images and improves the model’s reconstruction ability for such images. Meanwhile, the designed image correction penalty term restricts the difference between the generated images and the average image. This not only improves the stability of the optimization process but also reduces the convergence deviation. Experimental results demonstrate that IORN significantly improves the resolution and fidelity of reconstructed images during gradient inversion attacks on the MNIST, CIFAR-100, and LFW datasets, especially showing outstanding performance when dealing with large batches of complex images.

Aggregate signatures enable the sensor nodes of Industrial Internet of Things to send their signatures to the aggregator to realize signature compression. Before being stored in the data center, sensitive data and non-sensitive data should adopt different data processing methods in the process of sensor data fusion. In the high security analysis scenario of Industrial Internet of Things, only the verifier with a specified high security level can verify the resulting aggregate signature. So far, no one has explored how to ensure sensitive data privacy in the designated-verifier aggregate signatures. Motivated by it, this paper proposes a designated-verifier aggregate signature scheme (named DVAS) based on permissioned blockchain to achieve sensitive data privacy. In this scheme, the aggregator can be used not only to aggregate signatures, but also to sanitize data. Through smart contracts, the aggregator can sanitize the sensitive data according to the contract, and convert the original signature of the sensitive data into a valid signature. Therefore, DVAS can achieve elastic sensitive data privacy, not limited to encryption operations. The security attributes of DVAS include conditional anonymity, unforgeability, immutability and protecting data privacy. At the same time, DVAS realizes accountability through signature verification. Finally, the formal security proof, performance evaluation and experiments indicate that DVAS is secure, effective and practical for Industrial Internet of Things.

The Internet of Things (IoT) devices possessed by individuals produce massive amounts of data. The private data onto specific IoT devices can be combined with intelligent platform to provide help for future research and prediction. As an important digital asset, individuals can sell private data to get rewards. Problems, such as privacy, security, and access control prevent individuals from sharing their private data. The blockchain technology is widely used to build an anonymous trading system. In this article, we construct a blockchain-based privacy-preserving and rewarding private data-sharing scheme (BPRPDS) for IoT. A privacy issue worth considering is that the malicious cloud server may establish a behavior profile database of data users (DUs). In the case of anonymity, the transactions of private data sharing are easy to cause disputes. When anonymous DUs are framed, it is hard to protect their rights. With the help of the deniable ring signature and Monero, we realize the behavior profile building prevention and nonframeability of BPRPDS. At the same time, we utilize the licensing technology executed by smart contracts to ensure flexible access control of multisharing. The proposed BPRPDS is provably secure. Performance analysis and experimental results show that BPRPDS is efficient and practical.

In the digital twin environment, the fusion data onto physical entities in the physical space are mapped to multiple virtual spaces for digital modeling and intelligent simulation in different dimensions. In real intelligent manufacturing scenarios, heterogeneous multi-source fusion data are collected at the same time period. So they are consistent in time state. For the autonomous digital twin system, time states verification and integrity checking are basic security factors. Provable data possession technology can check the integrity of data onto virtual spaces. The blockchain can provide the synchronization interface to make distributed entities to obtain the trusted time state value. Considering the privacy, the blockchain can also provide anonymous services for entities. Therefore, we propose the blockchain-based synchronized provable data possession scheme (named BSPDP) for digital twin. In our scheme, the selection of verifier is flexible. Since virtual spaces may be maliciously framed to pay compensation, we use tag verification to prevent honest virtual spaces from being framed. Under the assumption of RSA, the proposed BSPDP is provably secure. Finally, the performance analysis demonstrates that BSPDP is practical. The experimental results show that BSPDP is effective and attractive for digital twin.

For large-scale data transmission of the Industrial Internet of Things (IIoT), aggregate signature is an effective approach. It can compress the signatures of different senders to save bandwidth. In order to maintain the autonomous management of IIoT, massive sensing data are sent to the data center for intelligent analysis. The reliability of data is an important guarantee of the autonomous management of IIoT. Tracing abnormal senders is a challenge when hiding their real identity. Therefore, we design the first permissioned blockchain-based anonymous and traceable aggregate signature (PBATAS) scheme for IIoT. Smart contracts are used to authenticate anonymous sources and share cryptographic materials among entities, providing reliable regulatory support for IIoT. The regulator can quickly trace the abnormal data sources recorded on the blockchain, which is practical for the anonymous IIoT environment. Through the formal security proof of conditional anonymity, unforgeability, traceability, and resistance to coalition attacks, the proposed PBATAS is provably secure. Performance analysis demonstrates that PBATAS is effective.