Generative artificial intelligence (AI) has become popular. The combination of increasingly complex datasets beyond human comprehension and the widespread availability of advanced computing systems—such as graphics processing unit (GPU) and tensor processing unit (TPU)—has driven the rapid advancement of generative AI. This technology has found applications in areas such as voice recognition, recommendation systems and data privacy preservation, which foster more data sharing and reuse. While challenges related to bias, fairness and uncertainty in AI continue to evolve, emerging government regulations aim to ensure ethical use and maximize societal benefits. In this paper, we present a system that leverages generative adversarial network (GAN) to enable privacy-preserving data publishing. The system supports contact tracing for infectious diseases like coronavirus disease 2019 (COVID-19) and monkey-pox. Evaluation using COVID-19 data highlights the practicality and effectiveness of our system.

Privacy policy, terms of use, public consent, reusable data, and transparency are trending words associated with the world wide web data such that privacy is now the responsibility of all stakeholders. Although privacy is a concern, integrating publicly available data may be for social good. For instance, integrating emergency calls, substance use, and overdose antagonist drug may help inform policies relating to emergency resources allocation, substance overdose antagonist drug distribution, and spiral effect of reducing overdose death. Hence, in this paper, we examine privacy preserving integration of public open data in the hierarchy of time and space. Our experimental result on four open datasets demonstrate the effectiveness of temporal and location hierarchy model in preserving privacy integration of big temporal co-occurrence data.

The era of big data is evolving with the introduction of the data lake concept. While a data warehouse provides a well-structured model to manage big data, a data lake accepts data of any types and formats with or without schema and provides access to the data for diverse communities of users. A data lake provides flexible, agile, and scalable solution to manage the ever-increasing volume of big data we are witnessing in the world today, including many siloed data collected over the years by researchers through Arctic expeditions. In this paper, we present our conceptual model of a data lake for integrating the diverse huge amount of data collected by researchers during Arctic expedition. We also design a baseline metadata using a data-driven approach to manage the disparately huge structured, semi-structured, and unstructured data collected from the Arctic region. The resulting open data lake not only effectively manages big Arctic data but also supports machine learning on these big data.

Partially due to technological advancements as well as the availability of affordable global positioning system (GPS) and cellular devices, more spatio-temporal data can be generated and collected. The presence of spatial and temporal dimensions uniquely differentiate spatio-temporal data from classical data as spatio-temporal data points are structurally related in the context of space and time. In this paper, we present a solution for privacy-preserving publishing and visualization of spatiotemporal big data information. Specifically, it consists of a spatiotemporal hierarchy model (STHM) for some common big data management tasks such as visualization. Our data visualizer provides actionable insight to enhance data-driven decision making. It also enables the discovery of hidden patterns, clusters of events, and outliers. We design two different metrics to preprocess the spatio-temporal for data visualization. Although we demonstrate the usefulness of our solution in privacy-preserving publishing and visualization of spatio-temporal information by using big real-life parking data from two cities, our solution can be applicable for publishing and visualizing spatio-temporal information from many other big data.

In the current technological era, huge amounts of big data are generated and collected from a wide variety of rich data sources. Embedded in these big data are useful information and valuable knowledge to be utilized. With the popularity of initiatives of open data, more big data have been published on open data platforms and made accessible to the public. To preserve privacy while maintaining the utility of data, research on privacy-preserving publishing has focused on preserving privacy of sensitive personal data such as patient data for health related applications. However, there are many other real-life situations, in which personal data of individual citizens and their daily routines need to be preserved when publishing. In this paper, we examine the problem of preserving privacy of temporal big data. Specifically, we present a temporal hierarchy privacy preserving model (THPPM) for some common daily routines-for example, parking. The model adapts and extends temporal hierarchy to generalize temporal data related to timestamp and spatial data related to check-in location. It also makes good use of generalized temporal representative points to preserve privacy of specific temporal data points. Evaluations on two real-life datasets on parking tickets for the US city of Buffalo and the Canadian city of Toronto shows that effectiveness and practicality of our THPPM in preserving privacy of temporal big data. Although this model is demonstrated and evaluated on parking ticket data, it would be applicable to preserving privacy of temporal big data for many other real-life applications and services.