The field of differential privacy is moving towards more efficient and accurate methods for private data analysis. Recent developments have focused on improving the trade-offs between privacy and utility, with a particular emphasis on reducing error bounds and increasing scalability. Notably, researchers are exploring new techniques such as continual counting and subsamplability to achieve stronger privacy guarantees. Additionally, there is a growing interest in applying zero-knowledge proofs to enable verifiable and privacy-preserving data verification. Noteworthy papers include: Differentially Private Quantiles with Smaller Error, which presents a mechanism for approximate quantiles with improved error bounds. Zk-SNARK for String Match, which proposes a secure and efficient string-matching platform leveraging zero-knowledge proofs. A Private Approximation of the 2nd-Moment Matrix of Any Subsamplable Input, which achieves strong privacy-utility trade-offs for second moment estimation. Verifying Differentially Private Median Estimation, which proposes the first verifiable differentially private median estimation scheme based on zero-knowledge proofs.