Cammarota, Rosario ; Schunter, Matthias ; Rajan, Anand ; Boemer, Fabian ; Kiss, Ágnes ; Treiber, Amos ; Weinert, Christian ; Schneider, Thomas ; Stapf, Emmanuel ; Sadeghi, Ahmad-Reza ; Demmler, Daniel ; Chen, Huili ; Hussain, Siam Umar ; Riazi, M. Sadegh ; Koushanfar, Farinaz ; Gupta, Saransh ; Rosing, Tajan Simunic ; Chaudhuri, Kamalika ; Nejatollahi, Hamid ; Dutt, Nikil ; Imani, Mohsen ; Laine, Kim ; Dubey, Anuj ; Aysu, Aydin ; Sadat Hosseini, Fateme ; Yang, Chengmo ; Wallace, Eric ; Norton, Pamela (2020)
Trustworthy AI Inference Systems: An Industry Research View.
doi: 10.48550/arXiv.2008.04449
Report, Bibliographie
Kurzbeschreibung (Abstract)
In this work, we provide an industry research view for approaching the design, deployment, and operation of trustworthy Artificial Intelligence (AI) inference systems. Such systems provide customers with timely, informed, and customized inferences to aid their decision, while at the same time utilizing appropriate security protection mechanisms for AI models. Additionally, such systems should also use Privacy-Enhancing Technologies (PETs) to protect customers' data at any time. To approach the subject, we start by introducing current trends in AI inference systems. We continue by elaborating on the relationship between Intellectual Property (IP) and private data protection in such systems. Regarding the protection mechanisms, we survey the security and privacy building blocks instrumental in designing, building, deploying, and operating private AI inference systems. For example, we highlight opportunities and challenges in AI systems using trusted execution environments combined with more recent advances in cryptographic techniques to protect data in use. Finally, we outline areas of further development that require the global collective attention of industry, academia, and government researchers to sustain the operation of trustworthy AI inference systems.
Typ des Eintrags: | Report |
---|---|
Erschienen: | 2020 |
Autor(en): | Cammarota, Rosario ; Schunter, Matthias ; Rajan, Anand ; Boemer, Fabian ; Kiss, Ágnes ; Treiber, Amos ; Weinert, Christian ; Schneider, Thomas ; Stapf, Emmanuel ; Sadeghi, Ahmad-Reza ; Demmler, Daniel ; Chen, Huili ; Hussain, Siam Umar ; Riazi, M. Sadegh ; Koushanfar, Farinaz ; Gupta, Saransh ; Rosing, Tajan Simunic ; Chaudhuri, Kamalika ; Nejatollahi, Hamid ; Dutt, Nikil ; Imani, Mohsen ; Laine, Kim ; Dubey, Anuj ; Aysu, Aydin ; Sadat Hosseini, Fateme ; Yang, Chengmo ; Wallace, Eric ; Norton, Pamela |
Art des Eintrags: | Bibliographie |
Titel: | Trustworthy AI Inference Systems: An Industry Research View |
Sprache: | Englisch |
Publikationsjahr: | 10 August 2020 |
Verlag: | arXiv |
Reihe: | Cryptography and Security |
Auflage: | 1. Version |
DOI: | 10.48550/arXiv.2008.04449 |
URL / URN: | https://arxiv.org/abs/2008.04449 |
Kurzbeschreibung (Abstract): | In this work, we provide an industry research view for approaching the design, deployment, and operation of trustworthy Artificial Intelligence (AI) inference systems. Such systems provide customers with timely, informed, and customized inferences to aid their decision, while at the same time utilizing appropriate security protection mechanisms for AI models. Additionally, such systems should also use Privacy-Enhancing Technologies (PETs) to protect customers' data at any time. To approach the subject, we start by introducing current trends in AI inference systems. We continue by elaborating on the relationship between Intellectual Property (IP) and private data protection in such systems. Regarding the protection mechanisms, we survey the security and privacy building blocks instrumental in designing, building, deploying, and operating private AI inference systems. For example, we highlight opportunities and challenges in AI systems using trusted execution environments combined with more recent advances in cryptographic techniques to protect data in use. Finally, we outline areas of further development that require the global collective attention of industry, academia, and government researchers to sustain the operation of trustworthy AI inference systems. |
Freie Schlagworte: | Primitives, P3, Solutions, S2, Engineering, E4 |
Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Praktische Kryptographie und Privatheit 20 Fachbereich Informatik > Systemsicherheit DFG-Sonderforschungsbereiche (inkl. Transregio) DFG-Sonderforschungsbereiche (inkl. Transregio) > Sonderforschungsbereiche DFG-Graduiertenkollegs DFG-Graduiertenkollegs > Graduiertenkolleg 2050 Privacy and Trust for Mobile Users Profilbereiche Profilbereiche > Cybersicherheit (CYSEC) LOEWE LOEWE > LOEWE-Zentren LOEWE > LOEWE-Zentren > CRISP - Center for Research in Security and Privacy DFG-Sonderforschungsbereiche (inkl. Transregio) > Sonderforschungsbereiche > SFB 1119: CROSSING – Kryptographiebasierte Sicherheitslösungen als Grundlage für Vertrauen in heutigen und zukünftigen IT-Systemen |
Hinterlegungsdatum: | 25 Aug 2020 07:57 |
Letzte Änderung: | 06 Aug 2024 09:13 |
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