The field of differential privacy is moving towards addressing the challenges posed by correlated and graph data. Researchers are developing new frameworks and mechanisms to provide rigorous privacy guarantees while preserving data utility. A key direction is the development of correlated-sequence differential privacy, which accounts for the temporal and cross-sequence links in multivariate streams. Another important area is the development of per-record differential privacy, which allows for varying privacy budgets across records. Additionally, there is a growing interest in node-differential privacy for graph analytics, with a focus on reducing node-DP tasks to edge-DP ones. Noteworthy papers in this area include: N2E: A General Framework to Reduce Node-Differential Privacy to Edge-Differential Privacy for Graph Analytics, which proposes a general framework for reducing node-DP graph analytical tasks to edge-DP ones. A General Framework for Per-record Differential Privacy, which proposes a practical framework that enables any standard DP mechanism to support per-record DP. Correlated-Sequence Differential Privacy, which introduces a framework specifically designed for preserving privacy in correlated sequential data.