Clustering algorithms are pivotal tools in data science and machine learning, offering a diverse array of applications ranging from customer segmentation to anomaly detection. With the development of cloud computing and outsourced computing, the adoption of clustering techniques has significantly accelerated. Despite the remarkable benefits of cloud computing, outsourcing sensitive data to remote cloud servers introduces considerable privacy and security concerns. Specifically, there is a risk that cloud service providers may engage in malicious behavior, such as data tampering. In response to these concerns, we propose four basic protocols based on vector space secret sharing, including secure Euclidean distance protocol, comparison protocol, minimum protocol, and division protocol. By applying these protocols, we construct a secure clustering scheme that can identify malicious behaviors. We thoroughly demonstrate the security of each underlying protocol as well as the overall clustering scheme. To validate the practicality and effectiveness of our approach, we conduct experiments on standard datasets. The results show that our clustering scheme performs efficiently while maintaining strong security guarantees.

Federated Learning (FL) has emerged as a promising paradigm to address data silos and privacy concerns in artificial intelligence applications. It shows great potential in the Internet of Vehicles (IoV), where sensitive vehicle data must remain local. However, FL is highly vulnerable to model poisoning attacks, especially under non-IID distributions and high malicious participation. Existing defenses, most relying on gradient similarity across clients, often fail under such heterogeneous settings. To address this, we propose RDFDS, a robust defense framework for FL in IoV. RDFDS consists of two tightly coupled modules: (1) a fast distillation-based synthesis module that accelerates knowledge distillation via a lightweight preprocessing step, suitable for real-time vehicular environments; (2) a Gas-KMeans clustering aggregation module that adaptively assigns aggregation weights by identifying the benign majority through Gap Statistics, preserving reliable contributions without requiring global or cross-client reference data. Extensive experiments on MNIST, CIFAR-10, and GTSRB demonstrate that RDFDS consistently outperforms existing defenses in accuracy and robustness, particularly under extreme non-IID conditions and high malicious participation, highlighting its practicality for real-world IoV scenarios.

Clustering algorithms, as the core technology in data analysis, can extract potential patterns and regularities from complex data. However, deploying k -means clustering on resource-limited devices remains a challenge. Despite the promise of cloud computing, outsourcing data to a remote cloud leads to high latency and privacy risks. Moreover, the stability and speed of cloud can be affected by the state of network and configuration, which leads to computation error. Therefore, we design a secure and dropout-resilient k -means clustering scheme based on cloud-edge-client collaboration architecture. In our scheme, cloud server simply generates multiplication triples in pre-processing phase and can be offline. In online phase, IoT devices secretly share the raw sensing data with three edge servers. Then edge servers accomplish the clustering task interactively. We propose four basic protocols based on vector space secret sharing, including Euclidean distance, comparison, minimum and division protocols. By applying these protocols, we construct a clustering scheme that can tolerate the exit of one edge server and corruption of two edge servers. Since edge servers are generally located in trusted environment, we allow them to reconstruct clustering result and provide low-latency and high-reliability service. We prove that the basic protocols and clustering scheme are secure against semi-honest adversary. We conduct the experiments on two realistic datasets, showing that our scheme has good efficiency and is suitable for practical application.

Advances in machine learning have enabled a broad range of complex applications, such as image recognition, recommendation system and machine translation. Data plays an important role in our increasingly complex and diverse environments, and this also reinforces the importance of data privacy in machine learning-enabled applications. Although there are a number of literature survey articles on machine learning, only a few studies have investigated the cryptographic primitives used in privacy-preserving machine learning (PPML). In other words, there is no, or limited, systematization of knowledge (SoK) that provides a comprehensive introduction to cryptography that have been deployed in PPML. In this paper, we firstly introduce some basic concepts such as machine learning tasks and processes. Then, we review and systematize the cryptographic primitives used in PPML. We analyze these existing privacy-preserving schemes in their learning process, especially training and inference. Finally, we conclude our survey and provide an outlook on future trends and research directions in the field.

Convolutional neural network (CNN) has attracted increasing attention and been widely used in imaging processing, bioinformatics and so on. As the cloud computing and multiparty computing are booming, the training and inference data of convolutional neural network often comes from diverse users. These users tend to jointly perform the computation but reluctantly share original data with others. Multi-key fully homomorphic encryption (MKFHE) supports homomorphic computation on ciphertexts encrypted with different keys, which is especially suitable for this scenario. In this paper, we firstly propose secure convolution, matrix multiplication, comparison and maximum protocols based on MKFHE. Then we design the secure CNN training and inference framework, outsourcing almost all computations to cloud server. To improve the efficiency, we use key switching technique for ciphertext transformation. We prove that the proposed frameworks are secure and feasible. The theoretical and experimental analysis show that our framework achieves the trade-off between security, efficiency and scalability.

Summary Difficulties in financing and low utilization of funds are main financial problems that plague the development of small and medium‐sized enterprises. The key to solving this problem lies in opening up the social data circulation between enterprises. It is a good solution for enterprises with frequent data interactions to form groups. Using group loans, the borrowing enterprises could solve the funding difficulties and the loan enterprises could improve the utilization rate of funds. In this article, we construct a group loan system based on blockchain technology, which can promote the free flow of funds among enterprises in the group. We combine the blockchain with the trusted execution environment to realize the automatic determination of loan conditions and realize the automatic execution of smart contracts. We also use the linkable group signature technology to ensure the traceability of loan users while protecting the anonymity. In addition, we use homomorphic encryption technology to make the statement confidential and computable.