The existing solutions related to local differential privacy (LDP) in multi-layer networks for edge computing scenarios present several limitations in both key-value data heavy hitter identification and related frequency and mean estimation tasks. First, existing LDP approaches cannot effectively use edge nodes to improve their utility/performance. Secondly, there are many network transmission tasks in edge computing, which have relatively high requirements for communication and storage costs. Furthermore, the traditional privacy budget allocation cannot attain the best utilization. To solve the above problems, we propose MLPKV, a local differential multi-layer private key-value data collection scheme for edge computing, structured into three phases: dimensional reduction, padding-length estimation, and estimation. An improved EC-OLH algorithm is used to offload the computing efforts related to aggregation and estimation to edge nodes for achieving greater efficiency. In the dimensional reduction phase, a candidate set is generated to prune the domain of original data, which improves the estimation. In addition, our method groups users for completing the tasks in each phase to avoid additional errors caused by dividing the privacy budget, and proposes a new user division with an optimal grouping ratio. Finally, the proposed method was implemented in a proof-of-concept prototype system. We compare MLPKV with baseline methods such as PrivKV and PCKV. Experimental results on both synthetic and real-world datasets show that our method achieves better utility for heavy hitter identification, frequency, and mean estimations than other state-of-the-art mechanisms. For small data sets, our approach also provides high-accuracy estimation with a low privacy budget.

中文翻译：

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MLPKV：一种面向边缘计算环境的局部差分多层私有键值数据采集方案

与边缘计算场景的多层网络中的局部差分隐私 (LDP) 相关的现有解决方案在键值数据重击者识别和相关频率和均值估计任务方面存在一些局限性。首先，现有的 LDP 方法不能有效地使用边缘节点来提高它们的效用/性能。其次，边缘计算中网络传输任务较多，对通信和存储成本要求比较高。此外，传统的隐私预算分配无法实现最佳利用。为了解决上述问题，我们提出了一种用于边缘计算的局部差分多层私钥-值数据收集方案MLPKV，其结构分为三个阶段：降维、填充长度估计和估计。改进的 EC-OLH 算法用于将与聚合和估计相关的计算工作卸载到边缘节点，以实现更高的效率。在降维阶段，生成一个候选集来修剪原始数据的域，从而提高估计。此外，我们的方法对完成每个阶段任务的用户进行分组，以避免划分隐私预算造成的额外错误，并提出具有最佳分组比例的新用户划分。最后，所提出的方法在概念验证原型系统中实现。我们将 MLPKV 与 PrivKV 和 PCKV 等基线方法进行比较。在合成数据集和真实数据集上的实验结果表明，与其他最先进的机制相比，我们的方法在重击者识别、频率和均值估计方面取得了更好的效用。