Integrating Differential Privacy and Contextual Integrity
Fuente:
arXiv
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917577193684992 |
|---|---|
| author | Benthall, Sebastian Cummings, Rachel |
| author_facet | Benthall, Sebastian Cummings, Rachel |
| contents | In this work, we propose the first framework for integrating Differential Privacy (DP) and Contextual Integrity (CI). DP is a property of an algorithm that injects statistical noise to obscure information about individuals represented within a database. CI defines privacy as information flow that is appropriate to social context. Analyzed together, these paradigms outline two dimensions on which to analyze privacy of information flows: descriptive and normative properties. We show that our new integrated framework provides benefits to both CI and DP that cannot be attained when each definition is considered in isolation: it enables contextually-guided tuning of the epsilon parameter in DP, and it enables CI to be applied to a broader set of information flows occurring in real-world systems, such as those involving PETs and machine learning. We conclude with a case study based on the use of DP in the U.S. Census Bureau. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_15774 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Integrating Differential Privacy and Contextual Integrity Benthall, Sebastian Cummings, Rachel Cryptography and Security Computers and Society In this work, we propose the first framework for integrating Differential Privacy (DP) and Contextual Integrity (CI). DP is a property of an algorithm that injects statistical noise to obscure information about individuals represented within a database. CI defines privacy as information flow that is appropriate to social context. Analyzed together, these paradigms outline two dimensions on which to analyze privacy of information flows: descriptive and normative properties. We show that our new integrated framework provides benefits to both CI and DP that cannot be attained when each definition is considered in isolation: it enables contextually-guided tuning of the epsilon parameter in DP, and it enables CI to be applied to a broader set of information flows occurring in real-world systems, such as those involving PETs and machine learning. We conclude with a case study based on the use of DP in the U.S. Census Bureau. |
| title | Integrating Differential Privacy and Contextual Integrity |
| topic | Cryptography and Security Computers and Society |
| url | https://arxiv.org/abs/2401.15774 |