Quantum-Secure Engine Enhances Privacy in Deep Learning

By Dr Silpaja Chandrasekar, PhDReviewed by Susha Cheriyedath, M.Sc.Oct 10 2024

In this study, researchers introduced a quantum-secure linear algebra engine for multiparty computation aimed at securing deep learning (DL) tasks while minimizing information leakage. The engine leverages standard telecommunication components, applying principles from quantum mechanics to enhance data privacy. The study’s key focus was to demonstrate how this quantum-based approach could support secure cloud-based deep learning with high accuracy and minimal risk of leaking sensitive information.

Background

Traditional methods of secure computation, such as homomorphic encryption, allow computations on encrypted data while preserving privacy. However, these methods often suffer from high computational overhead and potential security risks. Moreover, they typically rely on computational complexity for security, which is not as robust as information-theoretic security—a concept where security is guaranteed by the laws of physics rather than computational assumptions.

This study builds on recent advancements in optical deep neural networks (DNNs), which use integrated photonics and fiber optics to process data securely. The researchers proposed a delocalized optical DNN that protects client data while revealing only neural network weights to the client.

Quantum-Secure Computation Framework

The quantum-secure framework in this study was designed to prevent data and weight leakage during multiparty computations, especially in deep learning tasks. The engine achieved this by employing optical technology and leveraging the quantum properties of light. Specifically, the researchers used in-phase/quadrature (I/Q) modulation to encode neural network weights onto weak coherent states—a quantum state of light generated by attenuating a continuous-wave laser to the few-photon limit.

The setup involved two main components:

1. A transmitter that modulated the neural network weights onto quantum states.
2. A receiver that used homodyne detection to measure the quadratures (I/Q) of the incoming verification states, ensuring that the information remained secure during computation.

By using quantum principles, the researchers ensured that weight and data leakage were kept to a minimum while still allowing high-performance computations.

Information Leakage Boundaries

To quantify the security of the proposed engine, the study applied two key theoretical bounds:

1. Holevo Theorem: This theorem was used to set an upper bound on weight leakage, ensuring that dishonest clients could only extract a minimal amount of weight information.
2. Cramér-Rao Inequality: This inequality established a lower bound on data leakage, assessing the server’s ability to estimate the client’s input data during computations. The study demonstrated that data leakage was nearly zero when the gain in the client’s amplification step was set to one.

The results showed that weight leakage was kept below 0.1 bits per symbol, and data leakage was limited to 0.01 bits, far below the precision typically required for deep learning models.

Application to Deep Learning

The researchers tested the engine on the MNIST classification task, a common benchmark in machine learning, using a two-layer deep learning model. Despite the presence of quantum noise in the system, the model achieved over 96 % accuracy, comparable to traditional, non-secure methods.

Additionally, the team modeled the relationship between classification accuracy and various quantum parameters (such as gain and photon occupation) using a logistic function, showing that accuracy improved with these parameters and approached the levels of a digital noiseless system.

Security Analysis

The study’s security analysis was framed within the continuous-variable quantum key distribution (CVQKD) protocol, which is typically used in secure quantum communications. The analysis focused on protecting against both honest but curious participants and malicious attackers who might try to exploit quantum states to gather information.

The researchers addressed two key types of attacks:

1. Eavesdropping: Potential attackers intercepting quantum states during transmission. The analysis showed that the system's design, which used homodyne detection, prevented significant information leakage during such attacks.
2. Weight Accumulation Attacks: The researchers proposed strategies to prevent clients from accumulating weight information over multiple queries, ensuring that the neural network weights remained protected throughout the computations.

The results confirmed that the system provided strong security against both classical and quantum attacks, making it a robust solution for secure multiparty deep learning.

Conclusion

This study successfully introduced a quantum-secure linear algebra engine for multiparty computation in deep learning. The engine effectively minimized weight and data leakage during DL tasks, achieving over 96 % accuracy on the MNIST classification task while keeping leakage to less than 0.1 bits per symbol—a level much lower than that required for modern DL models.

By demonstrating a secure and efficient way to perform deep learning computations in a cloud-based environment, this research lays the foundation for privacy-preserving AI systems. Future work could explore the application of this quantum-secure framework to more complex models and tasks, further enhancing secure cloud-based deep learning.

Journal Reference

Sulimany, K., et al. (2024). Quantum-secure multiparty deep learning. ArXiv. DOI:10.48550/arXiv.2408.05629, https://arxiv.org/abs/2408.05629

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