We propose a new privacy-preserving mean-variance optimization model, merging Multi-Party Computation (MPC) with Homomorphic Encryption (HE) through an innovative method. Empirical tests show our model outperforms existing ones in privacy optimization, overcoming limitations regarding complex constraints. We highlight three findings: our model (i) outperforms others in privacy-preserving utility maximization with no-short-selling constraint; (ii) remains effective under complex box constraints, whereas the existing model entirely collapses; and (iii) achieves close alignment with the optimal portfolio from an economic perspective, providing high computational efficiency. It proves to be an effective solution for privacy optimization, a key aspect in mitigating ESG risks.

Co-authors: Hyungjin Ko, Jaewook Lee