SGXIO: Generic Trusted I/O Path for Intel SGX

Application security traditionally strongly relies upon security of the underlying operating system. However, operating systems often fall victim to software attacks, compromising security of applications as well. To overcome this dependency, Intel introduced SGX, which allows to protect application code against a subverted or malicious OS by running it in a hardware-protected enclave. However, SGX lacks support for generic trusted I/O paths to protect user input and output between enclaves and I/O devices. This work presents SGXIO, a generic trusted path architecture for SGX, allowing user applications to run securely on top of an untrusted OS, while at the same time supporting trusted paths to generic I/O devices. To achieve this, SGXIO combines the benefits of SGX's easy programming model with traditional hypervisor-based trusted path architectures. Moreover, SGXIO can tweak insecure debug enclaves to behave like secure production enclaves. SGXIO surpasses traditional use cases in cloud computing and makes SGX technology usable for protecting user-centric, local applications against kernel-level keyloggers and likewise. It is compatible to unmodified operating systems and works on a modern commodity notebook out of the box. Hence, SGXIO is particularly promising for the broad x86 community to which SGX is readily available.Comment: To appear in CODASPY'1

MLCapsule: Guarded Offline Deployment of Machine Learning as a Service

With the widespread use of machine learning (ML) techniques, ML as a service has become increasingly popular. In this setting, an ML model resides on a server and users can query it with their data via an API. However, if the user's input is sensitive, sending it to the server is undesirable and sometimes even legally not possible. Equally, the service provider does not want to share the model by sending it to the client for protecting its intellectual property and pay-per-query business model. In this paper, we propose MLCapsule, a guarded offline deployment of machine learning as a service. MLCapsule executes the model locally on the user's side and therefore the data never leaves the client. Meanwhile, MLCapsule offers the service provider the same level of control and security of its model as the commonly used server-side execution. In addition, MLCapsule is applicable to offline applications that require local execution. Beyond protecting against direct model access, we couple the secure offline deployment with defenses against advanced attacks on machine learning models such as model stealing, reverse engineering, and membership inference

Keys in the Clouds: Auditable Multi-device Access to Cryptographic Credentials

Personal cryptographic keys are the foundation of many secure services, but storing these keys securely is a challenge, especially if they are used from multiple devices. Storing keys in a centralized location, like an Internet-accessible server, raises serious security concerns (e.g. server compromise). Hardware-based Trusted Execution Environments (TEEs) are a well-known solution for protecting sensitive data in untrusted environments, and are now becoming available on commodity server platforms. Although the idea of protecting keys using a server-side TEE is straight-forward, in this paper we validate this approach and show that it enables new desirable functionality. We describe the design, implementation, and evaluation of a TEE-based Cloud Key Store (CKS), an online service for securely generating, storing, and using personal cryptographic keys. Using remote attestation, users receive strong assurance about the behaviour of the CKS, and can authenticate themselves using passwords while avoiding typical risks of password-based authentication like password theft or phishing. In addition, this design allows users to i) define policy-based access controls for keys; ii) delegate keys to other CKS users for a specified time and/or a limited number of uses; and iii) audit all key usages via a secure audit log. We have implemented a proof of concept CKS using Intel SGX and integrated this into GnuPG on Linux and OpenKeychain on Android. Our CKS implementation performs approximately 6,000 signature operations per second on a single desktop PC. The latency is in the same order of magnitude as using locally-stored keys, and 20x faster than smart cards.Comment: Extended version of a paper to appear in the 3rd Workshop on Security, Privacy, and Identity Management in the Cloud (SECPID) 201

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

SoK: A Systematic Review of TEE Usage for Developing Trusted Applications

Trusted Execution Environments (TEEs) are a feature of modern central processing units (CPUs) that aim to provide a high assurance, isolated environment in which to run workloads that demand both confidentiality and integrity. Hardware and software components in the CPU isolate workloads, commonly referred to as Trusted Applications (TAs), from the main operating system (OS). This article aims to analyse the TEE ecosystem, determine its usability, and suggest improvements where necessary to make adoption easier. To better understand TEE usage, we gathered academic and practical examples from a total of 223 references. We summarise the literature and provide a publication timeline, along with insights into the evolution of TEE research and deployment. We categorise TAs into major groups and analyse the tools available to developers. Lastly, we evaluate trusted container projects, test performance, and identify the requirements for migrating applications inside them.Comment: In The 18th International Conference on Availability, Reliability and Security (ARES 2023), August 29 -- September 01, 2023, Benevento, Italy. 15 page

Information Provenance for Mobile Health Data

Mobile health (mHealth) apps and devices are increasingly popular for health research, clinical treatment and personal wellness, as they offer the ability to continuously monitor aspects of individuals\u27 health as they go about their everyday activities. Many believe that combining the data produced by these mHealth apps and devices may give healthcare-related service providers and researchers a more holistic view of an individual\u27s health, increase the quality of service, and reduce operating costs. For such mHealth data to be considered useful though, data consumers need to be assured that the authenticity and the integrity of the data has remained intact---especially for data that may have been created through a series of aggregations and transformations on many input data sets. In other words, information provenance should be one of the main focuses for any system that wishes to facilitate the sharing of sensitive mHealth data. Creating such a trusted and secure data sharing ecosystem for mHealth apps and devices is difficult, however, as they are implemented with different technologies and managed by different organizations. Furthermore, many mHealth devices use ultra-low-power micro-controllers, which lack the kinds of sophisticated Memory Management Units (MMUs) required to sufficiently isolate sensitive application code and data. In this thesis, we present an end-to-end solution for providing information provenance for mHealth data, which begins by securing mHealth data at its source: the mHealth device. To this end, we devise a memory-isolation method that combines compiler-inserted code and Memory Protection Unit (MPU) hardware to protect application code and data on ultra-low-power micro-controllers. Then we address the security of mHealth data outside of the source (e.g., data that has been uploaded to smartphone or remote-server) with our health-data system, Amanuensis, which uses Blockchain and Trusted Execution Environment (TEE) technologies to provide confidential, yet verifiable, data storage and computation for mHealth data. Finally, we look at identity privacy and data freshness issues introduced by the use of blockchain and TEEs. Namely, we present a privacy-preserving solution for blockchain transactions, and a freshness solution for data access-control lists retrieved from the blockchain

Secure migration of WebAssembly-based mobile agents between secure enclaves

Cryptography and security protocols are today commonly used to protect data at-rest and in-transit. In contrast, protecting data in-use has seen only limited adoption. Secure data transfer methods employed today rarely provide guarantees regarding the trustworthiness of the software and hardware at the communication endpoints. The field of study that addresses these issues is called Trusted or Confidential Computing and relies on the use of hardware-based techniques. These techniques aim to isolate critical data and its processing from the rest of the system. More specifically, it investigates the use of hardware isolated Secure Execution Environments (SEEs) where applications cannot be tampered with during operation. Over the past few decades, several implementations of SEEs have been introduced, each based on a different hardware architecture. However, lately, the trend is to move towards architecture-independent SEEs. As part of this, Huawei research project is developing a secure enclave framework that enables secure execution and migration of applications (mobile agents), regardless of the underlying architecture. This thesis contributes to the development of the framework by participating in the design and implementation of a secure migration scheme for the mobile agents. The goal is a scheme wherein it is possible to transfer the mobile agent without compromising the security guarantees provided by SEEs. Further, the thesis also provides performance measurements of the migration scheme implemented in a proof of concept of the framework