ShieldDINC: Privacy-Preserving Distributed In-Network Computations with Efficient Homomorphic Encryption

The continuous growth of the Internet of Things (IoT) and its expanding network drives an exponential increase in data generation, which necessitates robust and efficient processing solutions for latency-sensitive applications. While local processing on resource-constrained devices can be inefficient, offloading computations to third parties introduces significant security and privacy risks. Homomorphic encryption (HE) offers a powerful solution for secure computations on encrypted data without compromising confidentiality. However, the computational intensity of HE results in high latency, limiting its applicability in real-time decision-making scenarios. To overcome these challenges, we propose ShieldDINC, a distributed in-network computation scheme designed for efficiency and privacy protection using localized edge processing to optimize secure implementations with HE. In addition, it leverages the efficient data delivery and caching capabilities inspired by an information-centric networking (ICN) approach. ShieldDINC ensures that sensitive data is securely offloaded and computed at the nearest edge router, which caches results for potential reuse, reducing retrieval time and repetitive computations. Extensive simulations demonstrate that ShieldDINC achieves a 30% latency reduction and a 20% computational efficiency improvement for real-time intensive computations compared to state-of-the-art alternatives while promising robust security and privacy.