Local Differential Privacy for Molecular Communication Networks

TL;DR

This work integrates 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, enabling privacy-preserving aggregate data analysis for in-body health monitoring and other population-scale sensing applications.

Abstract

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 k-ary Randomized Response (KRR) and Optimized Local Hashing (OLH) achieve the lowest average $\ell_1$ distribution-estimation error under the MC channel: OLH is preferable when channel resources are sufficient and the number of possible user values (alphabet size) $k$ is moderate to large, whereas the KRR is 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.

Figures (4)

• Figure 1: MC Channel Model
• Figure 2: Network architecture for local differential privacy in molecular communication
• Figure 3: Average $\ell_1$ losses across methods. Unless stated otherwise, $D=79.4~\mu\mathrm{m}^2/\mathrm{s}$, $r_R=5~\mu\mathrm{m}$, $r_0=10~\mu\mathrm{m}$$N=10^4$, $\sigma^2=0$.
• Figure 4: Average $\ell_1$ losses across methods. Unless stated otherwise, $k=16$, $D=79.4~\mu\mathrm{m}^2/\mathrm{s}$, $r_R=5~\mu\mathrm{m}$, $r_0=10~\mu\mathrm{m}$, $N=10^4$, $\sigma^2=0$.