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.