Biblio

The increasing data generation rate and the proliferation of deep learning applications have led to the development of machine learning-as-a-service (MLaaS) platforms by major Cloud providers. The existing MLaaS platforms, however, fall short in protecting the clients’ private data. Recent distributed MLaaS architectures such as federated learning have also shown to be vulnerable against a range of privacy attacks. Such vulnerabilities motivated the development of privacy-preserving MLaaS techniques, which often use complex cryptographic prim-itives. Such approaches, however, demand abundant computing resources, which undermine the low-latency nature of evolving applications such as autonomous driving.To address these challenges, we propose SCLERA–an efficient MLaaS framework that utilizes trusted execution environment for secure execution of clients’ workloads. SCLERA features a set of optimization techniques to reduce the computational complexity of the offloaded services and achieve low-latency inference. We assessed SCLERA’s efficacy using image/video analytic use cases such as scene detection. Our results show that SCLERA achieves up to 23× speed-up when compared to the baseline secure model execution.

As workloads and data move to the cloud, it is essential that software writers are able to protect their applications from untrusted hardware, systems software, and co-tenants. Intel® Software Guard Extensions (SGX) enables a new mode of execution that is protected from attacks in such an environment with strong confidentiality, integrity, and replay protection guarantees. Though SGX supports memory oversubscription via paging, virtualizing the protected memory presents a significant challenge to Virtual Machine Monitor (VMM) writers and comes with a high performance overhead. This paper introduces SGX Oversubscription Extensions that add additional instructions and virtualization support to the SGX architecture so that cloud service providers can oversubscribe secure memory in a less complex and more performant manner.