The field of information security and privacy is rapidly evolving, with a focus on developing new measures and frameworks to protect sensitive information. Recent research has highlighted the importance of considering dynamic leakage and information incompleteness in defending against stealth attacks. Additionally, there is a growing interest in developing new privacy-preserving algorithms and protocols, such as those based on pointwise maximal leakage and network oblivious transfer. Theoretical foundations, including language equivalence and parametric iteration in resource theories, are also being explored to support the development of more secure and private systems. Noteworthy papers include: The paper on A new measure for dynamic leakage based on quantitative information flow, which provides a novel definition of dynamic leakage and demonstrates its compatibility with the well-established static perspective. The paper on Learning to Attack: Uncovering Privacy Risks in Sequential Data Releases, which proposes a novel attack model that captures sequential dependencies in data releases and demonstrates the potential for privacy risks in sequential data publishing.