A Privacy-preserving mean-variance optimal portfolio

Abstract

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.