Privacy-preserving data aggregation in WBANs: a mixed-signal hardware architecture for lightweight homomorphic encryption
Daxing Wang, Wei Zhou, Liangli Du · Journal of King Saud University - Computer and Information Sciences · 2026
Abstract A novel hardware-enforced privacy protection scheme for Wireless Body Area Networks (WBANs) is proposed based on a mixed-signal lightweight homomorphic encryption architecture, addressing the challenge of patient data confidentiality in resource-constrained medical devices via analog-source integrated encryption. The core innovation is a Mixed-Signal to Ciphertext Converter (MS2CC) that replaces traditional analog-to-digital conversion with embedded encryption, enabling end-to-end data protection from acquisition to transmission. Designed in TSMC 65 nm LP CMOS technology with a 0.0286 mm 2 core area, the MS2CC achieves an ultra-low 3.66 μW in post-layout simulation with RC extraction when processing clinical-grade ECG signals, achieving a three-order-of-magnitude power reduction compared with software-based homomorphic encryption implementations. A digital equivalent of the LAHE algorithm was implemented on a Xilinx Zynq-7020 FPGA for functional validation, confirming correct encryption, homomorphic addition, and decryption operations. A tailored Lightweight Additive Homomorphic Encryption (LAHE) scheme is formalized for this architecture, with rigorous theoretical derivation validating its cryptographic security and approximate additive homomorphism with bounded error. Post-layout simulation results show a 48.2 dB signal-to-noise ratio for decrypted signals and 97.9% accuracy for privacy-preserving data aggregation, allowing untrusted aggregators to perform statistical ciphertext computations without accessing raw patient data and thus realizing the "honest-but-curious" security model. This work provides a promising new direction for ultra-low-power hardware security in medical IoT, bridging cryptographic theory and practical mixed-signal circuit implementation for next-generation privacy-aware healthcare monitoring systems.