You Still See Me: How Data Protection Supports the Architecture of AI Surveillance
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866916424927150080 |
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| author | Yew, Rui-Jie Qin, Lucy Venkatasubramanian, Suresh |
| author_facet | Yew, Rui-Jie Qin, Lucy Venkatasubramanian, Suresh |
| contents | Data forms the backbone of artificial intelligence (AI). Privacy and data protection laws thus have strong bearing on AI systems. Shielded by the rhetoric of compliance with data protection and privacy regulations, privacy-preserving techniques have enabled the extraction of more and new forms of data. We illustrate how the application of privacy-preserving techniques in the development of AI systems--from private set intersection as part of dataset curation to homomorphic encryption and federated learning as part of model computation--can further support surveillance infrastructure under the guise of regulatory permissibility. Finally, we propose technology and policy strategies to evaluate privacy-preserving techniques in light of the protections they actually confer. We conclude by highlighting the role that technologists could play in devising policies that combat surveillance AI technologies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_06609 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | You Still See Me: How Data Protection Supports the Architecture of AI Surveillance Yew, Rui-Jie Qin, Lucy Venkatasubramanian, Suresh Computers and Society Cryptography and Security Data forms the backbone of artificial intelligence (AI). Privacy and data protection laws thus have strong bearing on AI systems. Shielded by the rhetoric of compliance with data protection and privacy regulations, privacy-preserving techniques have enabled the extraction of more and new forms of data. We illustrate how the application of privacy-preserving techniques in the development of AI systems--from private set intersection as part of dataset curation to homomorphic encryption and federated learning as part of model computation--can further support surveillance infrastructure under the guise of regulatory permissibility. Finally, we propose technology and policy strategies to evaluate privacy-preserving techniques in light of the protections they actually confer. We conclude by highlighting the role that technologists could play in devising policies that combat surveillance AI technologies. |
| title | You Still See Me: How Data Protection Supports the Architecture of AI Surveillance |
| topic | Computers and Society Cryptography and Security |
| url | https://arxiv.org/abs/2402.06609 |