| Comparative study of differentially private data synthesis methods CMK Bowen, F Liu Statistical Science 35 (2), 280--307, 2020 | 85* | 2020 |
| Comparative Study of Differentially Private Synthetic Data Algorithms from the NIST PSCR Differential Privacy Synthetic Data Challenge CMK Bowen, J Snoke Journal of Privacy and Confidentiality 11 (1), 2021 | 48 | 2021 |
| Partitioning a large simulation as it runs K Myers, E Lawrence, M Fugate, CMK Bowen, L Ticknor, J Woodring, ... Technometrics 58 (3), 329-340, 2016 | 38* | 2016 |
| A synthetic supplemental public use file of low-income information return data: methodology, utility, and privacy implications CMK Bowen, V Bryant, L Burman, S Khitatrakun, R McClelland, ... Privacy in Statistical Databases: UNESCO Chair in Data Privacy …, 2020 | 21 | 2020 |
| A Feasibility Study of Differentially Private Summary Statistics and Regression Analyses with Evaluations on Administrative and Survey Data AF Barrientos, AR Williams, J Snoke, CMK Bowen Journal of the American Statistical Association 119 (545), 52-65, 2024 | 13 | 2024 |
| How statisticians should grapple with privacy in a changing data landscape J Snoke, CMK Bowen Chance 33 (4), 6-13, 2020 | 13 | 2020 |
| Differentially Private Data Release via Statistical Election to Partition Sequentially CMK Bowen, F Liu, B Su METRON, 2021 | 12* | 2021 |
| Philosophy of differential privacy CM Bowen, S Garfinkel Notices of the American Mathematical Society 68 (10), 2021 | 10 | 2021 |
| Synthetic individual income tax data: promises and challenges CMK Bowen, VL Bryant, L Burman, S Khitatrakun, R McClelland, ... National Tax Journal 75 (4), 767-790, 2022 | 9 | 2022 |
| The promise and limitations of formal privacy AR Williams, CMK Bowen Wiley Interdisciplinary Reviews: Computational Statistics, e1615, 2023 | 8 | 2023 |
| Synthetic individual income tax data: Methodology, utility, and privacy implications CMK Bowen, V Bryant, L Burman, J Czajka, S Khitatrakun, G MacDonald, ... International Conference on Privacy in Statistical Databases, 191-204, 2022 | 8 | 2022 |
| Protecting your privacy in a data-driven world CMK Bowen Chapman and Hall/CRC, 2021 | 6 | 2021 |
| A privacy-preserving validation server prototype S Taylor, G MacDonald, K Ueyama, CMK Bowen Technical Paper. Urban Institute, Washington, DC, 2021 | 6 | 2021 |
| Differentially private generation of social networks via exponential random graph models F Liu, E Eugenio, IH Jin, C Bowen 2020 IEEE 44th Annual Computers, Software, and Applications Conference …, 2020 | 6 | 2020 |
| Disclosing economists’ privacy perspectives: A survey of american economic association members on differential privacy and data fitness for use standards AR Williams, J Snoke, C Bowen, AF Barrientos National Bureau of Economic Research, 2023 | 5 | 2023 |
| Differentially private methods for validation servers AF Barrientos, AR Williams, J Snoke, CM Bowen Urban Institute research report, 2021 | 5* | 2021 |
| Personal Privacy and the Public Good: Balancing Data Privacy and Data Utility C Bowen Urban Institute, 2021 | 5 | 2021 |
| Decennial Disclosure C Bowen, AR Williams, M Pickens Urban Institute, 2022 | 4 | 2022 |
| Preserving privacy while sharing data SL Garfinkel, CMK Bowen MIT Sloan management review 63 (3), 1-4, 2022 | 4 | 2022 |
| The art of data privacy CMK Bowen Significance 19 (1), 14-19, 2022 | 4 | 2022 |
Joshua SnokeStatistician, RAND Corporation在 rand.org 的电子邮件经过验证
