With the continued proliferation of wireless communication and mobile devices equipped with built-in GPS sensors, burgeoning applications of location-based services are springing up, such as mobile crowdsourcing applications (MCS), which are revolutionizing our daily lives. However, collecting and sharing continuously spatio-temporal data to a service provider of MCS applications will incur users' concerns about their privacy. In this paper, we study the problem of locally differentially private data aggregation over distributed spatial-temporal infinite streams. To this end, we proposed a LDP-based framework for dealing with the problem. Firstly, we propose a novel model of (w, ε)-Clustering-based Local Differential Privacy ((w, ε)-CLDP) to capture the temporal and spatial correlations in spatio-temporal infinite stream while guaranteeing stringent differential privacy. Secondly, we develop an efficient GRR-based Local Budget Absorption (LBA) mechanism as a building block for achieving (w, ε)-CLDP and present its privacy analysis. On this basis, we present a framework of real-time spatio-temporal data aggregation over distributed infinite streams with an untrusted server. Lastly, we conduct experiments on two real-world datasets to validate our framework. The results manifest that the LBA-based framework is optimal in data utility for real-time spatio-temporal data aggregation with a rigorous privacy guarantee.

The Internet of Things (IoT) has created a ubiquitously connected world powered by a multitude of wired and wireless sensors generating a variety of heterogeneous data over time in a myriad of fields and applications. To extract complete information from these data, advanced artificial intelligence (AI) technology, especially deep learning (DL), has proved successful in facilitating data analytics, future prediction and decision making. The collective integration of AI and the IoT has greatly promoted the rapid development of AI-of-Things (AIoT) systems that analyze and respond to external stimuli more intelligently without involvement by humans. However, it is challenging or infeasible to process massive amounts of data in the cloud due to the destructive impact of the volume, velocity, and veracity of data and fatal transmission latency on networking infrastructures. These critical challenges can be adequately addressed by introducing edge computing. This article conducts an extensive survey of an end-edge-cloud orchestrated architecture for flexible AIoT systems. Specifically, it begins with articulating fundamental concepts including the IoT, AI and edge computing. Guided by these concepts, it explores the general AIoT architecture, presents a practical AIoT example to illustrate how AI can be applied in real-world applications and summarizes promising AIoT applications. Then, the emerging technologies for AI models regarding inference and training at the edge of the network are reviewed. Finally, the open challenges and future directions in this promising area are outlined.

Privacy protection is one of the major obstacles for data sharing. Time-series data have the characteristics of autocorrelation, continuity, and large scale. Current research on time-series data publication mainly ignores the correlation of time-series data and the lack of privacy protection. In this paper, we study the problem of correlated time-series data publication and propose a sliding window-based autocorrelation time-series data publication algorithm, called SW-ATS. Instead of using global sensitivity in the traditional differential privacy mechanisms, we proposed periodic sensitivity to provide a stronger degree of privacy guarantee. SW-ATS introduces a sliding window mechanism, with the correlation between the noise-adding sequence and the original time-series data guaranteed by sequence indistinguishability, to protect the privacy of the latest data. We prove that SW-ATS satisfies ε-differential privacy. Compared with the state-of-the-art algorithm, SW-ATS is superior in reducing the error rate of MAE which is about 25%, improving the utility of data, and providing stronger privacy protection.

With the advent of the era of big data, privacy issues have been becoming a hot topic in public. Local differential privacy (LDP) is a state-of-the-art privacy preservation technique that allows to perform big data analysis (e.g., statistical estimation, statistical learning, and data mining) while guaranteeing each individual participant’s privacy. In this paper, we present a comprehensive survey of LDP. We first give an overview on the fundamental knowledge of LDP and its frameworks. We then introduce the mainstream privatization mechanisms and methods in detail from the perspective of frequency oracle and give insights into recent studied on private basic statistical estimation (e.g., frequency estimation and mean estimation) and complex statistical estimation (e.g., multivariate distribution estimation and private estimation over complex data) under LDP. Furthermore, we present current research circumstances on LDP including the private statistical learning/inferencing, private statistical data analysis, privacy amplification techniques for LDP, and some application fields under LDP. Finally, we identify future research directions and open challenges for LDP. This survey can serve as a good reference source for the research of LDP to deal with various privacy-related scenarios to be encountered in practice.