Merging multi-source time series data in cloud servers significantly enhances the effectiveness of analyses. However, privacy concerns are hindering time series analytics in the cloud. Responsively, numerous secure time series analytics schemes have been designed to address privacy concerns. Unfortunately, existing schemes suffer from severe performance issues, making them impractical for real-world applications. In this work, we propose novel secure time series analytics schemes that break through the performance bottleneck by substantially improving both communication and computational efficiency without compromising security. To attain this, we open up a new technique roadmap that leverages the idea of mixed model. Specifically, we design a non-interactive secure Euclidean distance protocol by tailoring homomorphic secret sharing to suit subtractive secret sharing. Additionally, we devise a different approach to securely compute the minimum of three elements, simultaneously reducing computational and communication costs. Moreover, we delicately introduce a rotation concept, design a rotation-based hybrid comparison mode, and finally propose our fast secure top-$k$protocol that can dramatically reduce comparison complexity. With the above secure protocols, we propose a practical secure time series analytics scheme with exceptional performance and a security-enhanced scheme that considers stronger adversaries. Formal security analyses demonstrate that our proposed schemes can achieve the desired security requirements, while the comprehensive experimental evaluations illustrate that our schemes outperform the state-of-the-art scheme in both computation and communication.

With the rapid advancement and widespread ap plication of the graph neural network (GNN), the collaborative graph learning, in which multiple parties collaboratively construct a GNN model using their respective graph data, has attracted increasing attention. However, this paradigm also raises significant privacy concerns, as both nodes and edges may contain sensitive personal information, while existing privacy preserving schemes often come at the cost of degraded model performance or substantial system overhead. Therefore, this paper proposes an efficient and privacy-preserving collaborative, and Hui Li, Member, IEEE, Xiaoyu Kou Social Platform learning framework on vertically partitioned graph data, dubbed Plog. Specifically, we first design a decomposition algorithm to split the sparse adjacency matrix into the summation of multiple independent permutations, which are lightweight, parallelizable, and well-suited for secure multi-party computation. Building on this, a weighted oblivious batch permutation protocol is carefully customized based on correlated randomness to securely and efficiently compute adjacency matrix multiplications, addressing the core efficiency bottleneck in GNN inference and training. The selective security of Plog is formally verified under the ideal-real paradigm. Extensive experimental results on three real world datasets demonstrate that compared to the state-of-the art scheme, Plog can reduce online communication rounds by 46% and achieve a 1.73× speedup in the overall inference and training time.