Being part of public transportations, the bike sharing system plays an important role in solving the “last mile” problem. With more advantages than traditional bike sharing systems relying on fixed docks, dockless bike sharing systems are rapidly expanding, which generally operated under cloud‐based architectures. Emerging Things‐Edge‐Cloud (TEC) architectures are showing many advantages than cloud‐based architectures including greater potential in reducing risks of privacy leakage due to their decentralized computing manners. However, even in the TEC architecture, differential attacks, which aims to mine information of individuals in the aggregated data, still cause major threats. To solve this problem, this article proposes a novel encoding and decoding framework within the TEC architecture for time series predictions in bike sharing systems, with considerations of guaranteeing differential privacy. In particular, we first construct a dynamic autoencoder based on the Long Short Term Memory (LSTM) network, then the collected raw temporal data are encoded into a hidden state in the edge end. In the cloud end, the trained output weights are used to reconstruct the current time series and predict the next‐period time series according to received hidden states. This framework not only improves the computation efficiency of bike sharing system by leveraging computing power at the node end but also provides safer data publications that meet the differential privacy requirements. Experiments are conducted on real‐world datasets, results demonstrate the effectiveness of the proposed framework in providing data services with both high utility in sequence predictions and high safety level for users' privacy.

中文翻译：

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在共享单车系统中保证序列预测的差异隐私

作为公共交通的一部分，共享单车在解决“最后一公里”问题上发挥着重要作用。与依赖固定码头的传统共享单车系统相比，无桩共享单车系统正在迅速扩展，通常在基于云的架构下运行。Emerging Things-Edge-Cloud (TEC) 架构比基于云的架构显示出许多优势，包括由于其分散计算方式而在降低隐私泄露风险方面的更大潜力。然而，即使在 TEC 架构中，旨在挖掘聚合数据中个人信息的差异攻击仍然会造成重大威胁。为了解决这个问题，本文在 TEC 架构中提出了一种新颖的编码和解码框架，用于自行车共享系统中的时间序列预测，考虑到保证差异化隐私。特别是，我们首先基于长短期记忆（LSTM）网络构建了一个动态自编码器，然后将收集到的原始时间数据在边缘端编码为隐藏状态。在云端，训练好的输出权重用于重建当前时间序列，并根据接收到的隐藏状态预测下一周期的时间序列。该框架不仅通过利用节点端的计算能力提高了共享单车系统的计算效率，而且还提供了满足差异化隐私要求的更安全的数据发布。实验是在真实世界的数据集上进行的，