← 研踪 PaperTrailarXiv:2603.00690 · v2
Local Differential Privacy for Molecular Communication Networks
首次提交 2026-02-28 23:01
版本更新 2026-10-27 03:13
雷达观测 2026-10-29 00:35
摘要
Molecular communication (MC) enables information exchange in nanoscale sensor networks operating in biological environments, yet privacy remains largely unaddressed. We integrate local differential privacy (LDP) into diffusion-based MC by privatizing each user's measurement at the transmitter and conveying the resulting randomized report over the MC channel. To our knowledge, this is the first systematic LDP implementation for diffusion-based MC, enabling privacy-preserving aggregate data analysis for in-body health monitoring and other population-scale sensing applications. We benchmark major LDP mechanisms under a realistic channel model. Simulation results show that Optimized Local Hashing (OLH) achieves the lowest average $\ell_1$ distribution-estimation error under the MC channel when channel resources are sufficient and the number of possible user values (alphabet size) $k$ is moderate to large, whereas k-ary Randomized Response (KRR) and, for privacy budget $ε=1$, Binary Local Hashing (BLH), which have shorter reports, are more robust as the MC transmission quality deteriorates. We further propose RLIM-LDP, which combines run-length-limited ISI-mitigation (RLIM) coding with LDP coding. Extensive simulation results demonstrate that RLIM-LDP improves end-to-end reliability and reduces the final distribution-estimation error when time and molecule resources are limited.
作者备注：8 pages, 4 figures
观测到的版本
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v2 · 2026-10-27 03:13 · 最新观测版本
Local Differential Privacy for Molecular Communication Networks
Molecular communication (MC) enables information exchange in nanoscale sensor networks operating in biological environments, yet privacy remains largely unaddressed. We integrate local differential privacy (LDP) into diffusion-based MC by privatizing each user's measurement at the transmitter and conveying the resulting randomized report over the MC channel. To our knowledge, this is the first systematic LDP implementation for diffusion-based MC, enabling privacy-preserving aggregate data analysis for in-body health monitoring and other population-scale sensing applications. We benchmark major LDP mechanisms under a realistic channel model. Simulation results show that Optimized Local Hashing (OLH) achieves the lowest average $\ell_1$ distribution-estimation error under the MC channel when channel resources are sufficient and the number of possible user values (alphabet size) $k$ is moderate to large, whereas k-ary Randomized Response (KRR) and, for privacy budget $ε=1$, Binary Local Hashing (BLH), which have shorter reports, are more robust as the MC transmission quality deteriorates. We further propose RLIM-LDP, which combines run-length-limited ISI-mitigation (RLIM) coding with LDP coding. Extensive simulation results demonstrate that RLIM-LDP improves end-to-end reliability and reduces the final distribution-estimation error when time and molecule resources are limited.
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