Encrypted Large Model Inference: The Equivariant Encryption Paradigm (2502.01013v1)

Abstract: Large scale deep learning model, such as modern LLMs and diffusion architectures, have revolutionized applications ranging from natural language processing to computer vision. However, their deployment in distributed or decentralized environments raises significant privacy concerns, as sensitive data may be exposed during inference. Traditional techniques like secure multi-party computation, homomorphic encryption, and differential privacy offer partial remedies but often incur substantial computational overhead, latency penalties, or limited compatibility with non-linear network operations. In this work, we introduce Equivariant Encryption (EE), a novel paradigm designed to enable secure, "blind" inference on encrypted data with near zero performance overhead. Unlike fully homomorphic approaches that encrypt the entire computational graph, EE selectively obfuscates critical internal representations within neural network layers while preserving the exact functionality of both linear and a prescribed set of non-linear operations. This targeted encryption ensures that raw inputs, intermediate activations, and outputs remain confidential, even when processed on untrusted infrastructure. We detail the theoretical foundations of EE, compare its performance and integration complexity against conventional privacy preserving techniques, and demonstrate its applicability across a range of architectures, from convolutional networks to LLMs. Furthermore, our work provides a comprehensive threat analysis, outlining potential attack vectors and baseline strategies, and benchmarks EE against standard inference pipelines in decentralized settings. The results confirm that EE maintains high fidelity and throughput, effectively bridging the gap between robust data confidentiality and the stringent efficiency requirements of modern, large scale model inference.

• The paper introduces Equivariant Encryption to secure neural network inference while preserving nearly identical speed to unencrypted operations.
• It encrypts select layers to handle non-linear operations without approximation, thereby maintaining high model fidelity.
• The approach scales across diverse architectures and enables real-time analytics and decentralized deployments without hardware reliance.

Encrypted Large Model Inference: The Equivariant Encryption Paradigm

The paper under discussion introduces a sophisticated approach to privacy-preserving model inference applicable to LLMs and neural networks. As AI systems are increasingly deployed in sectors that demand stringent data privacy, such as healthcare and finance, the secure deployment of these models without sacrificing performance has become an imperative challenge. This paper presents Equivariant Encryption (EE) as a nuanced technique to address these privacy concerns while maintaining computational efficiency.

Overview of Equivariant Encryption

Equivariant Encryption is posited as an innovative method that differs from traditionally heavy cryptographic techniques like fully homomorphic encryption (FHE) and secure multi-party computation (SMPC). Instead of encrypting all operations, EE focuses on encrypting specific layers within neural networks to protect sensitive data without imposing significant computational overhead.

Key aspects of EE include:

• Minimal Latency Overhead: EE promises almost zero latency increase compared to unencrypted inference, a stark contrast to FHE, which often results in significant slowdowns due to complex computations.
• Robust Security without Trusted Hardware: Unlike Trusted Execution Environments (TEEs), which rely on hardware assumptions, EE operates without specialized hardware, reducing potential vulnerabilities associated with hardware trust models.
• Compatibility with Various Architectures: EE supports a range of operations within neural networks, including linear and common non-linear operations like ReLU and normalization layers, thereby maintaining accuracy and efficiency.

Comparative Analysis

The comparative strength of EE lies in its balance between security and performance:

• Latency and Scalability: As detailed in Table 1 of the paper, EE achieves latency similar to plaintext inference. This is a significant advantage over FHE, which remains resource-intensive due to its reliance on lattice-based cryptography.
• Handling of Non-linear Operations: EE manages non-linear operations without approximations, enhancing the precision of model outputs where HE might suffer from approximation errors.
• Scalability: The framework is inherently scalable to large models, a necessity in AI applications involving LLMs and comprehensive image processing.

Practical Deployments and Implications

The practical implications of EE extend across various use cases, notably in decentralized systems. The paper proposes scenarios where EE encrypts internal representations exchanged over distributed networks, such as blockchain-based infrastructures. By doing so, EE ensures that even if model queries and outputs traverse untrusted nodes, the data remains obscured from unauthorized access.

• Real-time Analytics: EE is particularly suited for applications where latency is critical, such as real-time AI analytics and LLMs deployed for conversational systems.
• Blockchain Systems: When integrated into blockchain systems, EE facilitates transactions involving confidential data, enhancing the privacy of operations across decentralized ledgers.

Security Analysis

The paper conducts a thorough threat analysis, outlining possible attacks that might aim to invert or bypass the EE protocol. It emphasizes the high-dimensionality and combinatorial complexity of EE's transformation process, which renders brute-force or direct inversion attacks computationally prohibitive. This intrinsic complexity is achieved without resorting to the large computational overhead associated with complete homomorphic encryption solutions.

Benchmarking Results

The empirical results presented in the paper demonstrate that EE maintains high fidelity in inference across tested models, as presented in Table 2. The fidelity score—a metric for the similarity between encrypted and non-encrypted inference result confidence values—indicates that EE delivers comparable outcomes to unencrypted models. Such results validate the claim that EE can preserve the accuracy of state-of-the-art models while enhancing privacy.

Conclusion and Future Work

This paper makes a significant contribution to the landscape of privacy-preserving AI applications, proposing a feasible and efficient alternative to existing cryptographic methods. The introduction of Equivariant Encryption not only bridges a crucial gap between performance and security in AI systems but also lays the groundwork for future research. Potential developments could explore further extensions into other types of AI models beyond LLMs and broader applications in complex multi-agent systems. The progressive refinement and adoption of EE in various domains might redefine standards for secure and efficient AI deployments.