Federated learning allows clients to share model gradients instead of privacy-sensitive data, which can solve the issue of data silos, but lead to the problem of data privacy leakage due to the model gradient revealing the characteristics of the training data. Privacy-preserving federated learning based on homomorphic encryption schemes (HE-based PPFL) can properly solve the issues of participantsfs data privacy leakage, but they encounter some new challenges. Existing PPFL-based single-key homomorphic encryption schemes face the problem that clients can obtain othersf model gradients due to the shared key and PPFL-based multi-key homomorphic encryption schemes face the issues of incomplete privacy protection for models and high communication overhead due to the requirement of the collaborated decryption. Moreover, existing PPFL schemes either assume the server is always honest or the verification method is unreliable and expensive. To tackle these emerging challenges in HE-based PPFL, we propose an enhancing privacy-preserving and verifiable federated learning scheme. Specifically, we first construct a novel multi-key homomorphic encryption algorithm that achieves single-key decryption instead of the collaborated decryption in traditional PPFL-based multi-key homomorphic encryption. Meanwhile, we design a blockchain-based public verification method for the global model by applying a vector homomorphic hash, which can properly solve the issues of unreliable and expensive global model verification of the existing global model verification methods. Formal security analysis shows that the proposed scheme can well provide complete privacy protection and guarantee the integrity of the global model. Extensive experiments demonstrate that the proposed schemes can keep high accuracy (≈95%) compared with existing differential privacy-based PPFL schemes (≤90%). Meanwhile, the proposed schemes can achieve no decryption share size (0MB) compared to existing HE-based PPFL schemes and efficient verification compared wit

Summary Data broadcast is a fundamental communication pattern in wireless IoT networks, in which the messages are disseminated from a source node to the entire network. The problem of minimum latency broadcast scheduling (MLBS) which is aimed to generate a quick and conflict‐free broadcast schedule has not been extensively explored in duty‐cycled networks. The existing works either work in a centralized scheme or rely on a fixed tree for broadcasting. Additionally, they all employ a strict premise that each node can only utilize one channel for both transmitting and receiving messages. Thus, to address the issues mentioned above, we examine the first distributed broadcasting algorithm in multi‐channel duty‐cycled wireless IoT networks, without relying on a predetermined tree. First, the MLBS problem in such networks is defined and proved to be NP‐hard. Then, in order to avoid transmission conflicts between different links locally, two efficient data structures are designed to help compute the earliest time and channel of receiving messages without conflicts. Based on the above data structures, we introduce an efficient distributed broadcasting algorithm, which can generate a latency‐sensitive broadcast tree while calculating a collision‐free broadcast schedule, simultaneously. Finally, the theoretical analysis and simulations demonstrate the efficiency of the proposed algorithm.

Compressed self-indexes are used widely in string processing applications, such as information retrieval, genome analysis, data mining, and web searching. The index not only indexes the data, but also encodes the data, and it is in compressed form. Moreover, the index and the data it encodes can be operated upon directly, without need to uncompress the entire index, thus saving time while maintaining small storage space. In some applications, such as in genome analysis, existing methods do not exploit the full possibilities of compressed self-indexes, and thus we seek faster and more space-efficient indexes. In this paper, we propose a practical high-order entropy-compressed self-index for efficient pattern matching in a text. We give practical implementations of compressed suffix arrays using a hybrid encoding in the representation of the neighbor function . We analyze the performance in theory and practice of our recommended indexing method, called GeCSA. We can improve retrieval time further using an iterated version of the neighbor function. Experimental results on the tested data demonstrate that the proposed index GeCSA has good overall advantages in space usage and retrieval time over the state-of-the-art indexing methods, especially on the repetitive data.