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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

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Published on: September 8, 2023

Round-Efficient Secure Inference Based on Masked Secret Sharing for Quantized Neural Network.

Weiming Wei^1,2, Chunming Tang^1,2, Yucheng Chen³

¹School of Mathematics and Information Science, Guangzhou University, Guangzhou 510006, China.

Entropy (Basel, Switzerland)

|February 25, 2023

Summary

This study introduces constant-round secure protocols for quantized neural network (QNN) inference using masked secret sharing (MSS). These protocols are practical for low-bandwidth, high-latency networks, overcoming previous limitations.

Keywords:

masked secret sharing quantized neural network secure inference

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Area of Science:

Cryptography
Machine Learning Security
Secure Computation

Background:

Secure multiparty computation (SMC) protocols based on secret sharing often require fast networks, limiting their real-world application.
Low-bandwidth and high-latency networks pose significant challenges for existing SMC schemes.

Purpose of the Study:

To develop constant-round secure protocols for quantized neural network (QNN) inference.
To enhance the practicality of SMC protocols for resource-constrained network environments.

Main Methods:

Utilized masked secret sharing (MSS) within a three-party honest-majority setting.
Designed protocols with a constant number of communication rounds.

Main Results:

Demonstrated the practicality of the developed protocols for QNN inference.
Validated suitability for networks characterized by low bandwidth and high latency.

Conclusions:

This work presents the first implementation of QNN inference using masked secret sharing.
The proposed constant-round protocols offer a viable solution for secure QNN inference in challenging network conditions.