Computation Storage Architectures (CSA) are increasingly adopted in the cloud for near data processing, where the underlying storage devices/servers are now equipped with heterogeneous cores which enable computation offloading near to the data. While CSA is a promising high-performance architecture for the cloud, in general data analytics also presents significant data security and policy compliance (e.g., GDPR) challenges in untrusted cloud environments. In this paper, we present IronSafe, a secure and policy-compliant query processing system for heterogeneous computational storage architectures, while preserving the performance advantages of CSA in untrusted cloud environments. To achieve these design properties in a computing environment with heterogeneous host (x86) and storage system (ARM), we design and implement the entire hardware and software system stack from the ground-up leveraging hardware-assisted Trusted Execution Environments (TEEs): namely, Intel SGX and ARM TrustZone. More specifically, IronSafe builds on three core contributions: (1) a heterogeneous confidential computing framework for shielded execution with x86 and ARM TEEs and associated secure storage system for the untrusted storage medium; (2) a policy compliance monitor to provide a unified service for attestation and policy compliance; and (3) a declarative policy language and associated interpreter for concisely specifying and efficiently evaluating a rich set of polices. Our evaluation using the TPC-H SQL benchmark queries and GDPR anti-pattern use-cases shows that IronSafe is faster, on average by 2.3x than a host-only secure system, while providing strong security and policy-compliance properties.

Arm TrustZone is a hardware technology that adds significant value to the ongoing security picture. TrustZone-based systems typically consolidate multiple environments into the same platform, requiring resources to be shared among them. Currently, hardware devices on TrustZone-enabled system-on-chip (SoC) solutions can only be configured as secure or non-secure, which means the dual-world concept of TrustZone is not spread to the inner logic of the devices. The traditional passthrough model dictates that both worlds cannot use the same device concurrently. Furthermore, existing shared device access methods have been proven to cause a negative impact on the overall system in terms of security and performance. This work introduces the concept of self-secured devices, a novel approach for shared device access in TrustZone-based architectures. This concept extends the TrustZone dual-world model to the device itself, providing a secure and non-secure logical interface in a single device instance. The solution was deployed and evaluated on the LTZVisor, an open-source and lightweight TrustZone-assisted hypervisor . The obtained results are encouraging, demonstrating that our solution requires only a few additional hardware resources when compared with the native device implementation, while providing a secure solution for device sharing.

Hundreds of millions of mobile devices worldwide rely on Trusted Execution Environments (TEEs) built with Arm TrustZone for the protection of security-critical applications (e.g., DRM) and operating system (OS) components (e.g., Android keystore). TEEs are often assumed to be highly secure; however, over the past years, TEEs have been successfully attacked multiple times, with highly damaging impact across various platforms. Unfortunately, these attacks have been possible by the presence of security flaws in TEE systems. In this paper, we aim to understand which types of vulnerabilities and limitations affect existing TrustZone-assisted TEE systems, what are the main challenges to build them correctly, and what contributions can be borrowed from the research community to overcome them. To this end, we present a security analysis of popular TrustZone-assisted TEE systems (targeting Cortex-A processors) developed by Qualcomm, Trustonic, Huawei, Nvidia, and Linaro. By studying publicly documented exploits and vulnerabilities as well as by reverse engineering the TEE firmware, we identified several critical vulnerabilities across existing systems which makes it legitimate to raise reasonable concerns about the security of commercial TEE implementations.

In the era of the Internet of Things (IoT), billions of wirelessly connected embedded devices rapidly became part of our daily lives. As a key tool for each Internet-enabled object, embedded operating systems (OSes) provide a set of services and abstractions which eases the development and speedups the deployment of IoT solutions at scale. This article starts by discussing the requirements of an IoT-enabled OS, taking into consideration the major concerns when developing solutions at the network edge, followed by a deep comparative analysis and benchmarking on Contiki-NG, RIOT, and Zephyr. Such OSes were considered as the best representative of their class considering the main key-points that best define an OS for resource-constrained IoT devices: low-power consumption, real-time capabilities, security awareness, interoperability, and connectivity. While evaluating each OS under different network conditions, the gathered results revealed distinct behaviors for each OS feature, mainly due to differences in kernel and network stack implementations.