Human-centered computing in cloud, edge, and fog is one of the most concerning issues. Edge and fog nodes generate huge amounts of data continuously, and the analysis of these data provides valuable information. But they also increase privacy risks. The personal sensitive data may be disclosed by untrusted third-party service providers, and the current solutions to privacy protection are inefficient, costly. It is difficult to obtain available statistics. To solve these problems, we propose a local differential privacy sensitive data collection protocol in human-centered computing. Firstly, to maintain high data utility, the selection of the optimal number of hash functions and the mapping length is based on the size of the collected data. Secondly, we hash the sensitive data, add the appropriate Laplace noise to the client side, and send the reports to the server side. Thirdly, we construct the count sketch matrix to obtain privacy statistics on the server side. Finally, the utility of the proposed protocol is verified by synthetic datasets and a real dataset. The experimental results demonstrate that the protocol can achieve a balance between data utility and privacy protection.

云，边缘和雾中以人为中心的计算是最令人关注的问题之一。边缘和雾节点连续生成大量数据，对这些数据的分析提供了有价值的信息。但是它们也会增加隐私风险。个人敏感数据可能会由不受信任的第三方服务提供商公开，并且当前的隐私保护解决方案效率低下，成本高昂。很难获得可用的统计数据。为了解决这些问题，我们提出了一种以人为中心的局部差分隐私敏感数据收集协议。首先，为了保持较高的数据实用性，散列函数的最佳数量和映射长度的选择基于收集到的数据的大小。其次，我们对敏感数据进行哈希处理，将适当的拉普拉斯噪声添加到客户端，并将报告发送到服务器端。第三，我们构造计数草图矩阵以获得服务器端的隐私统计信息。最后，通过综合数据集和真实数据集验证了所提出协议的实用性。实验结果表明，该协议可以在数据实用性和隐私保护之间取得平衡。