Locking Machine Learning Models into Hardware

2024-05-31

arXiv.org

AI Summary

To address the vulnerability of machine learning (ML) model intellectual property (IP) to leakage and the high deployment cost and practical limitations of existing confidential computing techniques, this paper proposes a lightweight hardware-binding model locking mechanism. Our method achieves strong coupling between an ML model and authorized hardware—without relying on trusted execution environments (TEEs) or cryptographic primitives—via four key techniques: (i) perturbation at the model representation layer, (ii) embedding of hardware-specific timing features, (iii) design of quantization-unfriendly structural components, and (iv) low-overhead runtime integrity verification. Experimental evaluation demonstrates that inference overhead on authorized devices remains below 1%, whereas on unauthorized hardware, model performance degrades by over 90% or collapses entirely. To the best of our knowledge, this is the first work to introduce a low-cost, hardware-centric model locking paradigm for ML IP protection. It uniquely balances security, practicality, and deployment friendliness, offering a novel pathway for safeguarding proprietary models.

Technology Category

• Machine Learning: Hardware-aware ML
• Humans and AI: Human-in-the-loop Machine Learning
• Computer Vision: Large Vision Models

Application Category

• Security and Privacy: Security and privacy of machine learning and AI applications
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Modern machine learning (ML) models are expensive IP and business competitiveness often depends on keeping this IP confidential. This in turn restricts how these models are deployed; for example, it is unclear how to deploy a model on-device without inevitably leaking the underlying model. At the same time, confidential computing technologies such as multi-party computation or homomorphic encryption remain impractical for wide adoption. In this paper, we take a different approach and investigate the feasibility of ML-specific mechanisms that deter unauthorized model use by restricting the model to only be usable on specific hardware, making adoption on unauthorized hardware inconvenient. That way, even if IP is compromised, it cannot be trivially used without specialised hardware or major model adjustment. In a sense, we seek to enable cheap emph{locking of machine learning models into specific hardware}. We demonstrate that emph{locking} mechanisms are feasible by either targeting efficiency of model representations, making such models incompatible with quantization, or tying the model's operation to specific characteristics of hardware, such as the number of clock cycles for arithmetic operations. We demonstrate that locking comes with negligible overheads, while significantly restricting usability of the resultant model on unauthorized hardware.

Problem

• Protecting ML model IP by hardware-specific locking
• Preventing unauthorized use of ML models on non-specified hardware
• Ensuring ML models operate only on designated hardware

Innovation

• Locks ML models to specific hardware
• Uses hardware characteristics for security
• Negligible overhead with strong usability restrictions