Recent financial sector changes, including strict privacy regulations, challenge robo-advisory companies with cybersecurity and data privacy. This study proposes a new framework integrating Homomorphic Encryption into the Black-Litterman portfolio model to safeguard robo-advisory investment strategies. The framework effectively balances privacy and accuracy while maintaining an acceptable level of privacy optimization error. Novel evaluation methods are also proposed to assess the trade-off between losses from privacy optimization and strategy leakage, from an economic viewpoint based on Expected Utility and Prospect Theory. It provides valuable insights into human behavior concerning privacy protection in portfolio management.

最近的金融业变化，包括严格的隐私法规，对机器人咨询公司的网络安全和数据隐私提出了挑战。本研究提出了一个将同态加密集成到 Black-Litterman 投资组合模型中的新框架，以保护机器人咨询投资策略。该框架有效地平衡了隐私和准确性，同时保持了可接受的隐私优化误差水平。还提出了新的评估方法，从基于预期效用和前景理论的经济学角度来评估隐私优化损失和策略泄漏之间的权衡。它为投资组合管理中有关隐私保护的人类行为提供了宝贵的见解。