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<p>Blockchain wallets use two primary key generation schemes: non-deterministic (ND) and hierarchical deterministic (HD). ND key generation scheme provides better fund distribution but has issues with backup complexity and memory utilization. HD key generation scheme simplifies the backup process but is vulnerable to privilege escalation and brute-force attacks. In addition, deterministic pseudo-random algorithms used in these key generation schemes are predictable, which makes Quantum Random Number Generators (QRNGs) a promising alternative. This paper proposes QaaS: a hybrid wallet based on user behavior that utilizes both HD and ND key generation architectures while leveraging QRNG to generate the keys. The wallet is optimized through deep learning, which trains on user behavior to select the optimal key generation scheme for maximum efficiency in blockchain wallet usage. We implemented and evaluated our proposed solution to support Ethereum transactions. Our results show that QaaS reduces risk by up to 98% compared to traditional HD wallets, while consuming similar memory resources.</p>

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Qaas: hybrid cryptocurrency wallet-as-a-service based on Quantum RNG

  • 期刊论文

  • 发表日期：

    2025年1月21日

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  • Muhammad Muneem Shabir,
  • Kaiwen Zhang,
  • Bertrand Reulet,
  • Ghyslain Gagnon

摘要

Blockchain wallets use two primary key generation schemes: non-deterministic (ND) and hierarchical deterministic (HD). ND key generation scheme provides better fund distribution but has issues with backup complexity and memory utilization. HD key generation scheme simplifies the backup process but is vulnerable to privilege escalation and brute-force attacks. In addition, deterministic pseudo-random algorithms used in these key generation schemes are predictable, which makes Quantum Random Number Generators (QRNGs) a promising alternative. This paper proposes QaaS: a hybrid wallet based on user behavior that utilizes both HD and ND key generation architectures while leveraging QRNG to generate the keys. The wallet is optimized through deep learning, which trains on user behavior to select the optimal key generation scheme for maximum efficiency in blockchain wallet usage. We implemented and evaluated our proposed solution to support Ethereum transactions. Our results show that QaaS reduces risk by up to 98% compared to traditional HD wallets, while consuming similar memory resources.