Entanglement-assisted authenticated BB84 protocol

TL;DR

This paper introduces a new entanglement-assisted authenticated BB84 quantum key distribution protocol that enhances security through classical and quantum memory assumptions, and demonstrates its effectiveness via simulation and machine learning techniques.

Contribution

The paper presents a novel entanglement-based authenticated QKD protocol with security proofs under specific assumptions and introduces a simulation with neural network-based authentication methods.

Findings

Secure authentication without noise under specific assumptions

Simulation shows >80% classification accuracy for adversary detection

Protocol allows secret key reusability and growth

Abstract

In this work, we present a novel authenticated Quantum Key Distribution (QKD) protocol employing maximally entangled qubit pairs. In the absence of noise, we securely authenticate the well-known BB84 QKD scheme under two assumptions: first, adversaries cannot simultaneously access preshared and non-pre-shared secret classical information, and second, adversaries cannot simultaneously access pre-shared secret classical information and quantum memories held by legitimate parties. The main strength of this noiseless result is that access to all secretly pre-shared classical information is insufficient for breaching our scheme. Additionally, our protocol desirably allows for pre-shared secrecy reusage, leading to secret key growing. In order to address noise, we simulate a photonic implementation of our scheme, together with a storage model that aims to replicate the performance of cavity-enhanced Atomic- Frequency Comb (AFC) memories. Two methods are used to distinguish authentic entities from forgery attempts: on the one hand, a statistical approach is used after calibration of its defining parameter $\mu$. Alternatively, a Deep Neural Network (DNN) is designed and trained to learn the underlying different structure of that input data coming from adversaries in comparison to that one coming from legitimate parties. Both methods achieve a correct classification rate larger than 0.80 for memory storage time of 150 $\mu$s and a 1 km distance between parties.