Position: Challenges and Opportunities for Differential Privacy in the U.S. Federal Government
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913559071424512 |
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| author | Khanna, Amol McCormick, Adam Nguyen, Andre Aguirre, Chris Raff, Edward |
| author_facet | Khanna, Amol McCormick, Adam Nguyen, Andre Aguirre, Chris Raff, Edward |
| contents | In this article, we seek to elucidate challenges and opportunities for differential privacy within the federal government setting, as seen by a team of differential privacy researchers, privacy lawyers, and data scientists working closely with the U.S. government. After introducing differential privacy, we highlight three significant challenges which currently restrict the use of differential privacy in the U.S. government. We then provide two examples where differential privacy can enhance the capabilities of government agencies. The first example highlights how the quantitative nature of differential privacy allows policy security officers to release multiple versions of analyses with different levels of privacy. The second example, which we believe is a novel realization, indicates that differential privacy can be used to improve staffing efficiency in classified applications. We hope that this article can serve as a nontechnical resource which can help frame future action from the differential privacy community, privacy regulators, security officers, and lawmakers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_16423 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Position: Challenges and Opportunities for Differential Privacy in the U.S. Federal Government Khanna, Amol McCormick, Adam Nguyen, Andre Aguirre, Chris Raff, Edward Cryptography and Security Artificial Intelligence Machine Learning In this article, we seek to elucidate challenges and opportunities for differential privacy within the federal government setting, as seen by a team of differential privacy researchers, privacy lawyers, and data scientists working closely with the U.S. government. After introducing differential privacy, we highlight three significant challenges which currently restrict the use of differential privacy in the U.S. government. We then provide two examples where differential privacy can enhance the capabilities of government agencies. The first example highlights how the quantitative nature of differential privacy allows policy security officers to release multiple versions of analyses with different levels of privacy. The second example, which we believe is a novel realization, indicates that differential privacy can be used to improve staffing efficiency in classified applications. We hope that this article can serve as a nontechnical resource which can help frame future action from the differential privacy community, privacy regulators, security officers, and lawmakers. |
| title | Position: Challenges and Opportunities for Differential Privacy in the U.S. Federal Government |
| topic | Cryptography and Security Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2410.16423 |