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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.

In the real world, a large number of multivariate time series data are generated by Internet of Things systems, which are composed of many connected sensing devices. Therefore, it is impractical to consider only a single univariate time series for decision-making. High-dimensional time series decrease the performance of traditional anomaly detection methods. Moreover, many previously developed methods capture temporal correlations instead of spatial correlations. Therefore, it is necessary to learn the temporal and spatial correlations between different time series and timestamps. In this paper, to achieve improved anomaly detection performance for multivariate time series, we propose a novel architecture based on a graph attention network (GAT) with multihead dynamic attention (MDA). This framework simultaneously learns the dependencies between sensors in both the temporal and spatial dimensions. To tackle the overfitting problem in autoencoder (AE)-based methods, we propose a hybrid approach that combines a novel generative adversarial network (GAN) architecture as a reconstruction model with a multilayer perceptron (MLP) as a prediction-based model to detect anomalies together. The detection framework proposed in this paper is called the HAD-multihead dynamic GAT (MDGAT). Extensive experiments on different public benchmarks demonstrate the superior performance of HAD-MDGAT over state-of-the-art methods.INDEX TERMS Multivariate time series, graph attention network, anomaly detection, deep generative model, gated recurrent unit.