Following strong regulations such as the European General Data Protection Regulation (GDPR), privacy protection in the financial sector has recently emerged as an urgent issue. To manage the privacy risk in robo-advisor, a representative fintech service, we propose a novel framework that allows robo-advisors to offer the optimal portfolio while complying with the privacy of their customers by encrypting individual risk aversion with homomorphic encryption (HE). By introducing an HE-friendly method for constrained optimization, our model can find a mean–variance quadratic programming solution even with inequality constraints. This study makes two main findings through empirical evaluation (i) our model can approximate optimal solution at an acceptable level of accuracy loss and the cost of preserving privacy, and (ii) the number of assets and the degree of correlation between assets affect the accuracy loss.

随着欧洲通用数据保护条例 (GDPR) 等强有力的法规的出台，金融领域的隐私保护最近成为一个紧迫的问题。为了管理代表性金融科技服务机器人顾问中的隐私风险，我们提出了一个新颖的框架，该框架允许机器人顾问提供最佳投资组合，同时通过使用同态加密 (HE) 加密个人风险规避来保护客户的隐私。通过引入一种 HE 友好的约束优化方法，我们的模型可以找到一个均值方差二次规划解决方案，即使不平等约束。本研究通过实证评估得出两个主要发现（i）我们的模型可以在可接受的精度损失水平和保护隐私成本下近似最优解，以及（ii）资产数量和资产之间的相关程度影响精度损失。