The field of secure computing and privacy-preserving technologies is rapidly advancing, with a focus on developing innovative solutions to protect sensitive data and ensure confidentiality. Recent developments have centered around improving the efficiency and scalability of homomorphic encryption, secure multi-party computation, and federated learning. Notably, researchers have made significant progress in designing hybrid homomorphic encryption frameworks, which combine the benefits of different encryption schemes to achieve better performance and security. Additionally, there have been advancements in secure inference and decoding methods for language models, enabling more efficient and private processing of sensitive data. Furthermore, novel secure aggregation methods have been proposed, utilizing the torus to guarantee perfect privacy for each party's data while avoiding accuracy losses. Overall, these advancements have the potential to significantly enhance the security and privacy of various applications, including machine learning, cloud computing, and data analytics. Noteworthy papers include: X-PRINT, which presents a server-centric framework for cross-platform fine-grained encrypted-traffic fingerprinting, and Efficient and High-Accuracy Secure Two-Party Protocols for a Class of Functions with Real-number Inputs, which proposes a generalized framework for designing secure protocols for a broad class of functions.