Local Pan-Privacy for Federated Analytics
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866929761441284096 |
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| author | Feldman, Vitaly McMillan, Audra Rothblum, Guy N. Talwar, Kunal |
| author_facet | Feldman, Vitaly McMillan, Audra Rothblum, Guy N. Talwar, Kunal |
| contents | Pan-privacy was proposed by Dwork et al. as an approach to designing a private analytics system that retains its privacy properties in the face of intrusions that expose the system's internal state. Motivated by federated telemetry applications, we study local pan-privacy, where privacy should be retained under repeated unannounced intrusions on the local state. We consider the problem of monitoring the count of an event in a federated system, where event occurrences on a local device should be hidden even from an intruder on that device. We show that under reasonable constraints, the goal of providing information-theoretic differential privacy under intrusion is incompatible with collecting telemetry information. We then show that this problem can be solved in a scalable way using standard cryptographic primitives. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_11850 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Local Pan-Privacy for Federated Analytics Feldman, Vitaly McMillan, Audra Rothblum, Guy N. Talwar, Kunal Cryptography and Security Data Structures and Algorithms Machine Learning Pan-privacy was proposed by Dwork et al. as an approach to designing a private analytics system that retains its privacy properties in the face of intrusions that expose the system's internal state. Motivated by federated telemetry applications, we study local pan-privacy, where privacy should be retained under repeated unannounced intrusions on the local state. We consider the problem of monitoring the count of an event in a federated system, where event occurrences on a local device should be hidden even from an intruder on that device. We show that under reasonable constraints, the goal of providing information-theoretic differential privacy under intrusion is incompatible with collecting telemetry information. We then show that this problem can be solved in a scalable way using standard cryptographic primitives. |
| title | Local Pan-Privacy for Federated Analytics |
| topic | Cryptography and Security Data Structures and Algorithms Machine Learning |
| url | https://arxiv.org/abs/2503.11850 |