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AIM: An Adaptive and Iterative Mechanism for Differentially Private Synthetic Data.

, , , and . Proc. VLDB Endow., 15 (11): 2599-2612 (2022)

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Benchmarking Differentially Private Synthetic Data Generation Algorithms., , , , and . CoRR, (2021)Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Data., , , , and . CoRR, (2024)ϵKTELO: A Framework for Defining Differentially Private Computations., , , , , , and . ACM Trans. Database Syst., 45 (1): 2:1-2:44 (2020)Graphical-model based estimation and inference for differential privacy., , and . ICML, volume 97 of Proceedings of Machine Learning Research, page 4435-4444. PMLR, (2019)EKTELO: A Framework for Defining Differentially-Private Computations., , , , , and . SIGMOD Conference, page 115-130. ACM, (2018)Permute-and-Flip: A new mechanism for differentially private selection., and . NeurIPS, (2020)Optimizing error of high-dimensional statistical queries under differential privacy., , , and . Proc. VLDB Endow., 11 (10): 1206-1219 (2018)Fair Decision Making using Privacy-Protected Data., , , , , and . CoRR, (2019)#8712;: A Framework for Defining Differentially-Private Computations., , , , , , and . SIGMOD Record, 48 (1): 15-22 (2019)(Amplified) Banded Matrix Factorization: A unified approach to private training., , , , , , and . CoRR, (2023)