GuaranTEE: Towards Attestable and Private ML with CCA (2404.00190v1)

Abstract: Machine-learning (ML) models are increasingly being deployed on edge devices to provide a variety of services. However, their deployment is accompanied by challenges in model privacy and auditability. Model providers want to ensure that (i) their proprietary models are not exposed to third parties; and (ii) be able to get attestations that their genuine models are operating on edge devices in accordance with the service agreement with the user. Existing measures to address these challenges have been hindered by issues such as high overheads and limited capability (processing/secure memory) on edge devices. In this work, we propose GuaranTEE, a framework to provide attestable private machine learning on the edge. GuaranTEE uses Confidential Computing Architecture (CCA), Arm's latest architectural extension that allows for the creation and deployment of dynamic Trusted Execution Environments (TEEs) within which models can be executed. We evaluate CCA's feasibility to deploy ML models by developing, evaluating, and openly releasing a prototype. We also suggest improvements to CCA to facilitate its use in protecting the entire ML deployment pipeline on edge devices.

• The paper introduces a framework that ensures complete ML model attestation and privacy on edge devices using Arm’s Confidential Computing Architecture.
• The authors design a dynamic Trusted Execution Environment, called realms, and demonstrate its feasibility on simulated Arm hardware using Fixed Virtual Platforms.
• Technical evaluation reveals a tradeoff of 1.62x performance overhead, highlighting the practical benefits of secure, tamper-proof ML execution.

An Evaluation of GuaranTEE: Attestable and Private Machine Learning on Edge Devices

The paper "GuaranTEE: Towards Attestable and Private ML with CCA" introduces GuaranTEE, a framework designed to tackle critical challenges in deploying ML models on edge devices, namely ensuring model privacy and auditability. The researchers leverage Arm's Confidential Computing Architecture (CCA), which allows for the creation of dynamic Trusted Execution Environments (TEEs) called realms, to run entire ML models in a secure and verifiable manner on edge devices.

Overview and Methodology

GuaranTEE is designed to address two primary concerns for ML model providers: maintaining the confidentiality of proprietary models and attesting that these models are not tampered with once deployed on client devices. The framework utilizes Arm's CCA, an architectural extension that supports TEEs, enabling the secure execution of ML models in encapsulated environments that protect against unauthorized access and modifications.

The authors develop and test a prototype of GuaranTEE using Arm's Fixed Virtual Platforms (FVP), allowing them to simulate the implementation and operation of the framework in an environment that mimics real-world conditions on CCA-compatible hardware.

Technical Evaluation

The paper provides a quantitative assessment of GuaranTEE's performance by comparing the overhead of running a TensorFlow Lite model for image recognition in both a field and a normal virtual machine. The results demonstrate that while there is an increase in the number of instructions required (1.62 times additional overhead), the benefits of secure execution and model protection justify this tradeoff.

Key technical contributions include:

• A demonstration that complete ML models can be executed within CCA's field, negating the need for complex model partitioning strategies typically necessary when balancing limited TEE memory with security requirements.
• The presentation of a systematic pipeline for attesting and executing ML models on edge devices, ensuring that models execute within their intended secure environments.

Implications and Future Directions

GuaranTEE represents a significant step toward secure and private ML deployments on edge devices, providing both theoretical and practical contributions to the field. By leveraging CCA, which is poised for widespread adoption due to its roots in existing Arm architectures, the framework addresses the critical need for secure, verifiable computation in resource-constrained environments.

Future developments in this area should focus on further reducing the performance overheads associated with secure model execution and expanding the capabilities of the CCA architecture to enhance both usability and security guarantees, particularly in multi-field and multi-tenant scenarios. Another promising direction is extending the architecture to protect not only the models but also the data pipelines themselves, mitigating risks from adversarial inputs and outputs.

Additionally, improved availability guarantees and robust mechanisms for policy enforcement could enhance trust between model providers and clients, making such frameworks more appealing for broader commercial deployment.

In summary, the introduction of GuaranTEE provides a robust framework for enhancing the security and privacy of ML models deployed on edge devices, with the potential for widespread application as supporting hardware and technology infrastructures become more prevalent.