TY - CHAP A1 - Bayerl, Sebastian P. A1 - Frassetto, Tommaso A1 - Jauernig, Patrick A1 - Riedhammer, Korbinian A1 - Sadeghi, Ahmad-Reza A1 - Schneider, Thomas A1 - Stapf, Emmanuel A1 - Weinert, Christian T1 - Offline Model Guard: Secure and Private ML on Mobile Devices T2 - 2020 Design, Automation & Test in Europe Conference & Exhibition (DATE) N2 - Performing machine learning tasks in mobile applications yields a challenging conflict of interest: highly sensitive client information (e.g., speech data) should remain private while also the intellectual property of service providers (e.g., model parameters) must be protected. Cryptographic techniques offer secure solutions for this, but have an unacceptable overhead and moreover require frequent network interaction.In this work, we design a practically efficient hardware-based solution. Specifically, we build OFFLINE MODEL GUARD (OMG) to enable privacy-preserving machine learning on the predominant mobile computing platform ARM—even in offline scenarios. By leveraging a trusted execution environment for strict hardware-enforced isolation from other system components, OMG guarantees privacy of client data, secrecy of provided models, and integrity of processing algorithms. Our prototype implementation on an ARM HiKey 960 development board performs privacy-preserving keyword recognition using TensorFlow Lite for Microcontrollers in real time. Y1 - 2020 U6 - https://doi.org/10.23919/DATE48585.2020.9116560 PB - IEEE ER - TY - CHAP A1 - Bayerl, Sebastian P. A1 - Brasser, Ferdinand A1 - Busch, Christoph A1 - Frassetto, Tommaso A1 - Jauernig, Patrick A1 - Kolberg, Jascha A1 - Nautsch, Andreas A1 - Riedhammer, Korbinian A1 - Sadeghi, Ahmad-Reza A1 - Schneider, Thomas A1 - Stapf, Emmanuel A1 - Treiber, Amos A1 - Weinert, Christian T1 - Privacy-preserving speech processing via STPC and TEEs (Poster) KW - Primitives; P3; Solutions; S2; Engineering; E4 Y1 - 2019 UR - tubiblio.ulb.tu-darmstadt.de/id/eprint/115811 ER -