Privacy-Preserving Federated Learning Framework for Secure Data Integration in Digital Real Estate Ecosystems

Abstract

Abstract: With real estate markets digitalizing at a remarkable pace, there's a growing — and largely unmet — need for machine learning systems that can harness multi-institutional data without putting privacy or regulatory standing at risk. In this paper, we present a Privacy-Preserving Federated Learning (PP-FL) framework built specifically for digital real estate ecosystems. Our approach lets distributed stakeholders — property agencies, government land registries, financial institutions, and PropTech platforms — collaboratively train predictive models without ever pooling their raw transaction or personal records in one place. We've designed the system around three interlocking privacy layers: a DP-SGD-based differential privacy optimizer, a homomorphic encryption scheme for gradient transmission, and a secure multi-party computation protocol to safeguard intermediate model states. On top of that, a blockchain-backed audit mechanism using zero-knowledge proofs provides verifiable, regulator-friendly compliance. When we tested the framework on a simulated dataset of 2.4 million real estate transactions spanning multiple institutional clients, it achieved 91.8% prediction accuracy — just 2.4 percentage points behind a fully centralized model — while holding the differential privacy budget to ε = 0.5, cutting communication overhead by 65% relative to naive federated approaches, and satisfying both GDPR and RERA requirements. We believe these results make a strong case that high-utility, privacy-first collaborative learning is not just theoretically possible but practically deployable in today's real estate sector. Keywords: Federated Learning, Differential Privacy, Homomorphic Encryption, Real Estate Analytics, Secure Multi-Party Computation, Blockchain, GDPR Compliance, Data Sovereignty, PropTech, Zero-Knowledge Proofs.