Abstract Privacy-preserving deep learning based on secure multi-party computation (MPC) has emerged as a critical research focus in recent years. While existing approaches predominantly employ additive secret sharing with a fixed number of parties, they have yet to fully leverage the more efficient Shamir-based schemes. However, the adoption of Shamir secret sharing faces two key challenges: limitations of decimal computation and signed number representation. Furthermore, current solutions often lack optimization for specific computational modules and rely on conventional methods ill-suited for MPC environments. To address these issues, this paper proposes a fixed-point decimal-supported Shamir secret sharing scheme. A key innovation is our truncation algorithm, which effectively manages the expanded decimal digits resulting from multiplication operations, enabling comprehensive fixed-point arithmetic within the Shamir-based MPC framework. Extensive large-scale simulations validate the accuracy of our truncation method. Moreover, we introduce optimized protocols for two crucial deep learning operations: convolution and Softmax function computation. Our convolution protocol leverages the Winograd algorithm to significantly reduce multiplication gate count, yielding over 50% performance improvement. For Softmax computation, we extend existing two-party protocols to a multi-party Shamir setting, developing the nQSMax algorithm. This algorithm achieves exceptional accuracy exceeding 99% within seconds, requiring only a few iterations.

With the continuous development of sensor technology, more and more users hope to monitor and collect information in a certain area safely and efficiently by deploying heterogeneous wireless sensor networks (HWSNs). However, nodes in HWSNs have limited capabilities, which leads to many security challenges. Existing data transmission schemes in HWSNs take measures to resist these security threats, which aggravate the node computation overhead and increase the network energy consumption. This paper proposes a Lightweight and Secure Data Transmission (LSDT) scheme against malicious nodes in heterogeneous wireless sensor networks. Firstly, considering node capabilities limitations in HWSNs, we design a lightweight secret sharing scheme based on XOR operation, which maps data to multiple shares and makes it convenient to transmit shares separately to the sink node via multiple paths. While guaranteeing data security, this scheme can greatly reduce the computation overhead of nodes compared with traditional secret sharing schemes. Further, during the delivery of shares, the network may be attacked by malicious nodes, causing the interruption of message transmission. Therefore, we design a malicious node detection and feedback mechanism, which can quickly respond to malicious node attacks and update the reputation degree of malicious nodes. Finally, we propose a routing selection scheme based on reference path which comprehensively considers the energy and reputation degree of heterogeneous nodes. It makes message transmission bypass malicious nodes while achieving network energy load balance, significantly extending the network lifetime. The security analysis proves that our scheme guarantees the security of data transmission. Theoretical analysis and experiments show that our scheme has significant advantages over the existing HWSNs data transmission schemes in terms of network lifetime extension and malicious node resistance.

The Internet of Medical Things (IoMT) is an important application of the Internet of Things in health care. In IoMT, efficiency and user privacy are crucial for cloud storage and retrieval of healthcare data documents. Existing schemes, however, often suffer from inefficient retrieval and increased risk of privacy disclosure when dealing with massive data. We propose here a new Efficient Encrypted Parallel Ranking (EEPR) search system, block-based and privacy-preserved, for encrypted cloud healthcare data. We design a parallel binary search tree structure in block and propose a parallel retrieval algorithm adaptable to such a structure. A quantitative analysis through the information retention index shows that our scheme demonstrates better search performance. In addition, feature vectors generated from our scheme are difficult to be reversely analyzed due to unexplainability, enhancing privacy protection for patients and researchers. A formal security analysis shows that our EEPR scheme is resistable to known background attack, and yields a lower time complexity and significantly improves search efficiency as well as accuracy over existing schemes.

With the rapid development of 5G/6G, most Internet of Things (IoT) devices will embrace wireless connection in the near future. A public concern is how to securely organize, store and retrieve data generated from IoT devices. Many cloud-based IoT data storage schemes have been proposed recently. However, for an untrusted or vulnerable cloud server, the stored IoT data can be easily accessed, modified and even destroyed given that the IoT data are stored in total centralization. Moreover, the servers in a cloud are generally homogeneous and thus vulnerable to attacks. For improvements, we design a novel framework for secure and efficient IoT data storage based on secret sharing and a collaborative blockchain. First, an ultra-lightweight secret sharing algorithm is designed to map original messages generated by IoT devices to a set of shorter message shares. Second, all the shares of IoT messages are separately delivered to different clouds for storage. To guarantee the security of shares, the delivery is notarized on a proposed blockchain. Specifically, both hash values of the shares and their information of location are embedded in blocks which are then chained to form a blockchain. Third, we create a balanced index structure about the shares for each cloud storage node based on the information in the blockchain, and we also propose a depth-first data search algorithm to improve IoT data retrieval efficiency. Theoretical analysis and simulation results illustrate that our scheme can store and retrieve the IoT data securely and efficiently.

With the rapid development of science and technology, a large amount of electronic waste is inevitably generated from various discarded and End-Of-Life electronic products. If these products are not handled properly, they can cause environmental pollution as well as loss of resources. As an important part of remanufacturing, disassembly is usually done manually with low efficiency and high labor cost. In this paper, parallel disassembly lines with multiple robots are proposed. These robots can run automatically and be used to perform disassembly in an optimal disassembly mode. A multitype robot can be flexibly set with multiple functions. A mathematical model is established to assign disassembly tasks to the robots such that a line can achieve the maximum profit and minimum carbon emissions. An improved multi-objective multi-verse optimizer is proposed and applied to a set of instances. Experimental results show that the algorithm has an overwhelming performance advantage over the other three commonly-used algorithms in solving this problem. It has better performance than the other peer algorithms in solving parallel disassembly line balancing problems.

As traditional computing and cloud computing integrate, the Internet of Things (IoT) has evolved into a layered and cloud-network-edge-end architecture. However, most searchable encryption models still use triples, in which hierarchical structures are neglected, and insecure intermediate nodes are exposed to external environment. Meanwhile, mainstream schemes adopting accurate retrieval are incompatible with IoT end users' features of differentiation. To address these issues, we innovatively design an efficient and credible search model with an accurate multikeyword fuzzy ciphertext retrieval scheme in the context of IoT. First, based on network coding and key sharing, data are grouped, encoded, and transmitted in parallel to the receiver node through middle-layer nodes, with high efficiency and reliability. Second, to realize fuzzy retrieval of IoT, edit distance is selected as the standard of difference between keywords, and then document index vector and query vector are created based on locality sensitive hashing (LSH) and Bloom Filter. Furthermore, to improve the traditional scheme, query keywords are split into multiple single-word forms, inner products between each trapdoor of single word and encryption index vector are calculated, respectively, for the sum of each inner product and thus top $\mathrm{top}$ - k $k$ sorting search. Ultimately, feasibility, safety, and efficiency of our improved scheme are verified by security analysis, while simulation results support that our scheme has better accuracy and efficiency.