How To Think About End-To-End Encryption and AI: Training, Processing, Disclosure, and Consent
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arXiv
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| Format: | Preprint |
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2024
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| author | Knodel, Mallory Fábrega, Andrés Ferrari, Daniella Leiken, Jacob Hou, Betty Li Yen, Derek de Alfaro, Sam Cho, Kyunghyun Park, Sunoo |
| author_facet | Knodel, Mallory Fábrega, Andrés Ferrari, Daniella Leiken, Jacob Hou, Betty Li Yen, Derek de Alfaro, Sam Cho, Kyunghyun Park, Sunoo |
| contents | End-to-end encryption (E2EE) has become the gold standard for securing communications, bringing strong confidentiality and privacy guarantees to billions of users worldwide. However, the current push towards widespread integration of artificial intelligence (AI) models, including in E2EE systems, raises some serious security concerns. This work performs a critical examination of the (in)compatibility of AI models and E2EE applications. We explore this on two fronts: (1) the integration of AI "assistants" within E2EE applications, and (2) the use of E2EE data for training AI models. We analyze the potential security implications of each, and identify conflicts with the security guarantees of E2EE. Then, we analyze legal implications of integrating AI models in E2EE applications, given how AI integration can undermine the confidentiality that E2EE promises. Finally, we offer a list of detailed recommendations based on our technical and legal analyses, including: technical design choices that must be prioritized to uphold E2EE security; how service providers must accurately represent E2EE security; and best practices for the default behavior of AI features and for requesting user consent. We hope this paper catalyzes an informed conversation on the tensions that arise between the brisk deployment of AI and the security offered by E2EE, and guides the responsible development of new AI features. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_20231 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | How To Think About End-To-End Encryption and AI: Training, Processing, Disclosure, and Consent Knodel, Mallory Fábrega, Andrés Ferrari, Daniella Leiken, Jacob Hou, Betty Li Yen, Derek de Alfaro, Sam Cho, Kyunghyun Park, Sunoo Cryptography and Security Artificial Intelligence End-to-end encryption (E2EE) has become the gold standard for securing communications, bringing strong confidentiality and privacy guarantees to billions of users worldwide. However, the current push towards widespread integration of artificial intelligence (AI) models, including in E2EE systems, raises some serious security concerns. This work performs a critical examination of the (in)compatibility of AI models and E2EE applications. We explore this on two fronts: (1) the integration of AI "assistants" within E2EE applications, and (2) the use of E2EE data for training AI models. We analyze the potential security implications of each, and identify conflicts with the security guarantees of E2EE. Then, we analyze legal implications of integrating AI models in E2EE applications, given how AI integration can undermine the confidentiality that E2EE promises. Finally, we offer a list of detailed recommendations based on our technical and legal analyses, including: technical design choices that must be prioritized to uphold E2EE security; how service providers must accurately represent E2EE security; and best practices for the default behavior of AI features and for requesting user consent. We hope this paper catalyzes an informed conversation on the tensions that arise between the brisk deployment of AI and the security offered by E2EE, and guides the responsible development of new AI features. |
| title | How To Think About End-To-End Encryption and AI: Training, Processing, Disclosure, and Consent |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2412.20231 |