Quantum Hacking on an Integrated Continuous-Variable Quantum Key Distribution System via Power Analysis.

Area of Science:

• Quantum Information Science
• Cybersecurity
• Integrated Photonics

Background:

• Practical quantum key distribution (QKD) systems have security vulnerabilities not present in theoretical models.
• Eavesdroppers can exploit these loopholes to steal secret key information undetected.
• Integrated silicon photonic continuous-variable quantum key distribution (CVQKD) systems are susceptible to novel quantum hacking strategies.

Purpose of the Study:

• To introduce a novel quantum hacking attack targeting integrated silicon photonic CVQKD systems.
• To demonstrate a power analysis attack leveraging machine learning on electrical control circuits.
• To assess the impact of this attack on secret key security.

Main Methods:

• A power analysis attack model was developed for CVQKD systems.
• Machine learning, specifically a support vector regression (SVR) algorithm, was employed to analyze power consumption.
• Simulations were conducted to evaluate the attack's effectiveness and impact on secret key secrecy.

Main Results:

• The proposed power analysis attack successfully extracts secret key information by analyzing power signals from the state preparation's electrical control circuit.
• Increased attack accuracy leads to a decrease in secret key information, particularly in low-noise environments.
• The attack does not require physical intrusion into the transmitter chip and is potentially applicable to discrete-variable QKD (DVQKD) systems.

Conclusions:

• Practical CVQKD systems face significant security risks from power analysis attacks.
• Enhancements to electrical control circuits to randomize power consumption are crucial for defense.
• Dynamic voltage and frequency scaling (DVFS) can mitigate power consumption and potentially reduce attack effectiveness.