The field of secure computing is moving towards more efficient and scalable solutions for privacy-preserving technologies. Recent developments have focused on optimizing system latency, improving the performance of secure inference frameworks, and enhancing the security of edge computing applications. Notably, advancements in homomorphic encryption, garbled circuits, and secure multi-party computation have enabled faster and more secure data processing. Furthermore, the integration of blockchain technology with edge computing has improved the robustness and security of Internet of Vehicles applications. Researchers have also explored the use of large language models for communication encryption and proposed novel architectures for efficient and secure data processing. Overall, these innovations have the potential to significantly impact the field of secure computing and enable more widespread adoption of privacy-preserving technologies. Noteworthy papers include: SecONNds, which achieves a 17x online speedup in nonlinear operations for secure inference; ReDASH, which provides a 33-fold speedup in overall inference time compared to existing frameworks; and SecFwT, which enables efficient privacy-preserving fine-tuning of large language models.