The field of privacy-preserving data publishing is moving towards developing more robust and scalable methods for protecting sensitive information while maintaining data utility. Recent research has focused on improving existing privacy models, such as k-anonymity and differential privacy, and developing new approaches that can balance privacy protection and utility preservation. Notable advancements include the development of modular and hybrid execution engines that can exploit multi-core parallelism and dynamic suppression budget management. Additionally, there is a growing interest in correlation-based data masking and semantic reformulation of k-anonymity to offer more robust privacy without losing utility. Noteworthy papers include: Core Mondrian, which presents a scalable extension of the Original Mondrian partition-based anonymization algorithm with a modular strategy layer and hybrid recursive/queue execution engine. Aegis, which introduces a middleware framework for identifying the optimal masking configuration for machine learning datasets that consist of features and a class label.