The field of private information retrieval and secure computing is rapidly advancing, with a focus on developing innovative solutions to protect sensitive information. Recent research has explored new approaches to symmetric private information retrieval, differential privacy, and secure vector retrieval. Notable developments include the design of efficient algorithms for private information retrieval on graph-based replicated systems and the introduction of novel security analytical frameworks. Additionally, researchers have made significant progress in optimizing tree-structure indexes for heterogeneous memory and developing secure protocols for oblivious pseudorandom functions. These advancements have the potential to significantly impact various applications, including AI, data integration, and secure computing. Noteworthy papers include: The Capacity of Semantic Private Information Retrieval with Colluding Servers, which derives the exact capacity of Sem-TPIR. Differentially Private Synthetic Graphs Preserving Triangle-Motif Cuts, which presents the first DP mechanism for generating synthetic graphs that approximate triangle-motif sizes of all cuts of a given graph. GATEBLEED: Exploiting On-Core Accelerator Power Gating for High Performance & Stealthy Attacks on AI, which discovers a timing side and covert channel caused by aggressive power gating in AI accelerators.