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.

翻译：分子通信（MC）实现了在生物环境中运行的纳米级传感器网络的信息交换，然而其隐私问题在很大程度上尚未得到解决。我们将本地差分隐私（LDP）集成到基于扩散的MC中，方法是在发射端对每个用户的测量值进行隐私化处理，并通过MC信道传输由此产生的随机化报告。据我们所知，这是首个针对基于扩散的MC的系统性LDP实施方案，为体内健康监测和其他群体规模传感应用实现了隐私保护的聚合数据分析。我们在一个现实的信道模型下对主要的LDP机制进行了基准测试。仿真结果表明，在MC信道下，k元随机响应（KRR）和优化本地哈希（OLH）实现了最低的平均$\ell_1$分布估计误差：当信道资源充足且用户可能取值的数量（字母表大小）$k$处于中等至较大范围时，OLH更优；而当MC传输质量恶化时，KRR则表现出更强的鲁棒性。我们进一步提出了RLIM-LDP，它将游程受限的ISI抑制（RLIM）编码与LDP编码相结合。大量的仿真结果表明，在时间和分子资源有限的情况下，RLIM-LDP提高了端到端的可靠性并降低了最终的分布估计误差。