Hongchen Guo

Currently, decentralized redactable blockchains have been widely applied in IoT systems for secure and controllable data management. Unfortunately, existing works ignore policy privacy (i.e., the content of users’ redaction policies), causing severe privacy leakage threats to users since users’ policies usually contain large amounts of private information (e.g., health conditions and geographical locations) and limiting the applications in IoT systems. To bridge this research gap, we propose PFRB, a policy-hidden fine-grained redactable blockchain in decentralized blockchain-based IoT systems. PFRB follows the decentralized settings and fine-grained chameleon hash-based redaction in existing redactable blockchains. In addition, PFRB hides users’ policies during policy matching such that apart from successful policy matching, users’ policy contents cannot be inferred and valid redactions cannot be executed. Some main technical challenges include determining how to hide policy contents and support policy matching. Inspired by Newton’s interpolation formula-based secret sharing, PFRB converts policy contents into polynomial parameters and utilizes multi-authority attribute-based encryption to further hide these parameters. Theoretical analysis proves the correctness and security against the chosen-plaintext attack. Extensive experiments on the FISCO blockchain platform and IoT devices show that PFRB achieves competitive efficiency over current redactable blockchains.

The redactable blockchain has emerged as a promising technique in mobile crowdsensing, allowing users to break immutability in a controlled manner selectively. Unfortunately, current fine-grained redactable blockchains suffer two significant limitations in terms of security and functionality, which severely impede their application in mobile crowdsensing. For security, the transparency of the blockchain allows anyone to access both the data and policy, which consequently results in a breach of user privacy. Regarding functionality, current solutions cannot support error tolerance during policy matching, thereby limiting their applicability in various situations, such as fingerprint-based and face-based identification scenarios. This paper presents a privacy-preserving fine-grained redactable blockchain with policy fuzzy matching, named PRBFM. PRBFM supports fuzzy policy matching and partitions users’ privileges without compromising user privacy. The idea of PRBFM is to leverage threshold linear secret sharing based on the Lagrange interpolation theorem to distribute the decryption keys and chameleon hash trapdoors. Additionally, we have incorporated a privacy-preserving policy matching delegation mechanism into PRBFM to minimize user overhead. Our security analysis demonstrates that PRBFM can defend against the chosen-ciphertext attack. Moreover, experiments conducted on the FISCO blockchain platform show that PRBFM is at least 7.8 times faster than existing state-of-the-art solutions.

The traditional centralized power trading model suffers from high maintenance costs, low processing efficiency and unsynchronized information, and it cannot adapt to the high-frequency and small-dollar distributed energy trading scenario. To address the above issues, we propose Trust-DETM, a model for the implementation of distributed energy trading based on a trusted execution environment. First, we introduce a reputation metric mechanism and propose a transaction matching algorithm based on the reputation metric to achieve the accurate matching of transaction objects. Secondly, as the distributed energy trading model lacks an effective trust mechanism, we propose a commitment scheme based on smart contracts and a trusted execution environment to solve the trust problem between producers and consumers. Finally, we conduct a comprehensive experimental evaluation of the efficiency of Trust-DETM. Through comparative experiments, we find that Trust-DETM achieves trade matching and trusted execution in a lower simultaneous running time than comparable distributed trading models.

As deep neural networks (DNNs) are widely used in the field of remote sensing image recognition, there is a model security issue that cannot be ignored. DNNs have been shown to be vulnerable to small perturbations in a large number of studies in the past, and this security risk naturally exists in remote sensing object detection models based on DNNs. The complexity of remote sensing object detection models makes it difficult to implement adversarial attacks on them, resulting in the current lack of systematic research on adversarial examples in the field of remote sensing image recognition. In order to better deal with the adversarial threats that remote sensing image recognition models may confront and to provide an effective means for evaluating the robustness of the models, this paper takes the adversarial examples for remote sensing image recognition as the research goal and systematically studies vanishing attacks against a remote sensing image object detection model. To solve the problem of difficult attack implementation on remote sensing image object detection, adversarial attack adaptation methods based on interpolation scaling and patch perturbation stacking are proposed in this paper, which realizes the adaptation of classical attack algorithms. We propose a hot restart perturbation update strategy and the joint attack of the first and second stages of the two-stage remote sensing object detection model is realized through the design of the attack loss function. For the problem of the modification cost of global pixel attack being too large, a local pixel attack algorithm based on sensitive pixel location is proposed in this paper. By searching the location of the sensitive pixels and constructing the mask of attack area, good local pixel attack effect is achieved. Experimental results show that the average pixel modification rate of the proposed attack method decreases to less than 4% and the vanishing rate can still be maintained above 80%, which effectively achieves the balance between attack effect and attack cost.