DRCF: A Privacy-Enhanced Distributed Publish-Subscribe System for Secure Data Computation in Untrusted Environments

Modern publish-subscribe systems are increasingly deployed in applications such as smart grids, industrial IoT, and smart homes, where brokers perform not only message forwarding but also in-broker computation. This shift from receive-forward (RF) to receive-compute-forward (RCF) models introduces new privacy and trust concerns, as centralized brokers gain access to sensitive data. In this paper, we present \(\mathsf{DRCF}\) , a distributed and privacy-preserving RCF framework that replaces the trust assumption on a single centralized broker with multiple semi-honest brokers. Our system integrates distributed authenticated encryption, threshold signatures, and two-party secure computation realized using homomorphic encryption and garbled circuits to protect both communication and computation. We formally prove the security of \(\mathsf{DRCF}\) in the semi-honest adversarial model. We implement \(\mathsf{DRCF}\) on top of the Mosquitto broker and evaluate it on real-world IoT workloads, including anomaly detection, load forecasting, and electricity price prediction. Experimental results demonstrate that \(\mathsf{DRCF}\) achieves scalability and efficiency while preserving privacy, confidentiality and integrity in modern pub-sub systems.