Offline Model Guard: Secure and Private ML on Mobile Devices

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.Comment: Original Publication (in the same form): DATE 202

Proposal and Validation of an Immersed Interface Method applied to the Lattice-Boltzmann Method

The Lattice Boltzmann Method (LBM) is an appealing framework to apply to unsteady, incompressible, low Reynolds number flow due to its simplicity and potential for massive parallelisation. The immersed boundary method is often used in conjunction with the LBM to simulate flow around curved, moving boundaries in the interior of the fluid domain. In the immersed boundary method, the boundary is imposed on a non-conforming grid by applying an external forcing at the boundary. The distribution of this external forcing is calculated based on interpolation of nearby fluid nodes, and the force field is then distributed over a number of nearby fluid nodes.The immersed interface method is an approach similar to the immersed boundary method, but imposes this applied force field directly on the solution field through jump conditions. It has yet to be correctly applied to the LBM framework. In this thesis, a proposal of an immersed interface method in the LBM is made, and its implementation is validated and compared against the immersed boundary method. To aid in this, a solver capable of solving fluid-structure interaction is developed.It is shown that the immersed interface method in the LBM has significant benefits compared to the immersed boundary method, in particular with regards to reducing numerical oscillations in the spatial variation of the boundary force distribution, as well appearing to yield a slight increase in overall accuracy at the same level of grid refinement.Aerospace Engineerin