Na Li

This paper proposes a framework to examine how neighborhood factors influence criminal justice (CJ) contact and contribute to disparities across multiple stages of the justice process. By conceptualizing the punishment process as a dynamic set of decision-making points, this study highlights the role of neighborhood context in shaping offenders’ CJ trajectories and post-CJ residential inequality. Using Harris County, Texas, as a case study, this research considers individual-, neighborhood-, and event-level variables to understand the cumulative effects of neighborhood characteristics on CJ outcomes. This study underscores the critical need to investigate neighborhood mobility and its broader implications for community development and public policy. The findings can be supported by extensive data from the Federal Statistical Research Data Centers and the Criminal Justice Administrative Records System, offering a robust analysis of offenders’ spatial patterns and economic transitions.

In the era of digitalization, a massive amount of data has been generated from people’s online activities or use of portable/wearable devices. The data often carries rich information about people. Therefore, privacy technologies are needed, from data generation to usage and from transmission to storage, to protect people’s sensitive information. Although the research community is making great progress in addressing advanced privacy protection technologies, very few educational materials have been developed to incorporate the latest research results and engage students in learning privacy technologies, especially for younger generations. In this paper, we present our newly designed educational materials on privacy technologies, which can be used for training high quality cybersecurity professionals to meet the ever-increasing demand. The developed learning modules not only incorporate the latest research results in privacy technologies but also include effective hand-on lab activities. To help other institutions effectively teach privacy technologies, we organized a faculty training workshop in summer 2022. Twenty-nine faculty from twenty institutions nationwide participated in the training. Survey results show that the participants gained a better understanding of privacy issues and demonstrated strong interest in teaching privacy technologies after attending the workshop.

With the increasing demand for a digital world, the Industrial Internet of Things (IIoT) is growing rapidly across various industries. In manufacturing, particularly in Industry 4.0, the IIoT assumes a vital role. It encompasses many devices such as sensing devices, application servers, users, and authentication servers within workshop settings. The security of the IIoT is a critical issue due to wireless networks’ open and dynamic nature. Therefore, designing secure protocols among those devices is an essential aspect of IIoT security functionality and poses a significant challenge to the IIoT systems. In this paper, we propose a lightweight anonymous authentication protocol to preserve privacy for IIoT users, enabling secure IIoT communication. The protocol has been validated to demonstrate its comprehensive ability to overcome various vulnerabilities and prevent malicious attacks. Finally, the performance evaluation confirms that the proposed protocol is more effective and efficient than the existing alternatives.