With the frequent occurrence of private data breaches, it is now more necessary than ever to address how to protect private data. The combination of Ciphertext-Policy Attribute-Based Encryption (CP-ABE) and blockchain typically enables secure storage and sharing of data. However, in high-dimensional attribute domains, that is, the number of attributes is large, these schemes have issues such as low security of data protection, high computational overhead, and high cost of attribute revocation. This paper proposes a personal privacy data protection scheme for encryption and revocation of high-dimensional attribute domains to address these issues. The proposed scheme is made up of three components. Firstly, Fast High-dimensional Attribute Domain-based Message Encryption (HAD-FME) is proposed to improve data security and reduce computational cost. Secondly, an Attribute Revocation Mechanism Based on Sentry Mode (SM-ARM) is designed in combination with smart contracts. Lastly, a Blockchain-based Model for Personal Privacy Data Protection (BC-PPDP) is proposed by integrating HAD-FME with SM-ARM. The security analysis results show that HAD-FME proposed in this paper is secure under the DLIN assumption, and the attribute revocation satisfies both forward and backward security. Experiments show that HAD-FME has higher computational efficiency than existing schemes in the high-dimensional attribute domains, SM-ARM has lower revocation cost than existing attribute revocation mechanisms, and smart contracts and blockchain work well.

Payment channel networks (PCNs) are one of the most promising off‐chain solutions to the blockchain scalability problem. However, most current payment channel schemes suffer from overly ideal routing assumptions or fail to provide complete path privacy protection. To address the above problems, the multi‐hop anonymous and privacy‐preserving PCN scheme are analyzed and a multi‐hop anonymous PCN based on onion routing is proposed based on it. The scheme removes the routing assumptions of the original scheme, designs an onion routing‐based payment channel network, and modifies the way relay nodes construct elliptic curve discrete logarithmic problems to resist wormhole attacks. Finally, the security analysis shows that the scheme in this paper has atomicity, relational anonymity and resistance to wormhole attacks. The latest bilinear pairing anonymous multi‐hop payment (EAMHL+) scheme is used to compare communication and computing overhead. The results show that the scheme in this paper requires less communication overhead and is more efficient in terms of computational overhead when the average hop number of the payment channel network is 3 hops and the network diameter is 6 hops.

The data hierarchical Ciphertext-Policy Attribute-Based Encryption (CP-ABE) scheme implements multiple hierarchical data encryption of a single access policy, which reduces the computation and storage overhead. However, existing data hierarchical CP-ABE schemes have some problems, such as the leakage of personal privacy information through access policies or user attributes in plaintext form, and these schemes grant enough privileges to a single authorization center. If the authorization center is untrusted or attacked, keys can be used to illegally access data, which is the key escrow problem. To solve these problems, we propose an Attribute Hiding and Multiple Authorization Centers-based Data Hierarchical Encryption Scheme (AH-MAC-DHE). Firstly, we propose an Attribute Convergence Hiding Mechanism (ACHM). This mechanism solves the problem of personal privacy information leakage by hiding access policies and user attributes. Secondly, we design Privilege-Dispersed Multiple Authorization Centers (PD-MAC). PD-MAC solves the problem of key escrow by dispersing the privileges of the single authorization center to the user authorization center and attribute authorization center. Finally, we prove that AH-MAC-DHE is secure under the decisional q-parallel Bilinear Diffie-Hellman Exponent (BDHE) assumption, which also satisfies anti-collusion and privacy security. The experimental results indicate that compared with existing schemes, AH-MAC-DHE performs well.

Untargeted poisoning attacks pose a serious threat to federated learning. However, existing untargeted poisoning attacks have limitations. Most attacks assume that the adversary can control a large number of real clients, which is difficult to achieve in practice. Although the poisoning attack based on fake clients overcomes dependence on real clients. It causes the model to classify all data into default categories, which limits the effectiveness of the attack. Additionally, the fake local model updates are consistent, making them easily detectable by existing defenses. the attack is less stealth. To address these issues, we propose an Untargeted Poisoning Attack based on Fake Clients called UPA-FC. The attack manipulates key model layers based on their importance to enhance its effectiveness. We also introduce a random flipping strategy to reduce similarity between fake local updates, enhancing the stealth of the attack. To defend against UPA-FC, we propose a clustering-based defense scheme called D-UPA-FC. This scheme analyzes the distance matrix using a clustering algorithm. It determines the optimal clusters by calculating Euclidean distances to aggregate the global model. Experimental results show that UPA-FC outperforms the existing poisoning attack in terms of both effectiveness and stealth, while D-UPA-FC effectively defends against the UPA-FC.

Although the decentralized storage technology based on the blockchain can effectively realize secure data storage on cloud services. However, there are still some problems in the existing schemes, such as low storage capacity and low efficiency. To address related issues, we propose a novel decentralized storage framework, which mainly includes four aspects: (1) we proposed a Bi-direction Circular Linked Chain Structure (BCLCS), which improves data's storage capacity and applicability in decentralized storage. (2) A Proof of Resources (PoR) decision model is proposed. By introducing the network environment as an essential evaluation parameter of storage right decision, the energy and time consumption of decision-making are reduced, and the fairness of decision-making is improved. (3) A chain structure dynamic locking mechanism (CSDLM) is designed to realize anti-traverse and access control. (4) A Bi-directional data Access Mechanism (BDAM) is proposed, which improves the efficiency of data access and acquisition in decentralized storage mode. The experimental results show that the framework has significantly improved the shortcomings of the current decentralized storage.