Track Co-Chairs
Nan Zhang
Professor
andyzhang@hit.edu.cn
Harbin Institute of Technology
Zilong Liu
Professor
Zilonglord@126.com
Dongbei University of Finance & Economics
Bailing Liu
Professor
bl_liu@ccnu.edu.cn
Central China Normal University
Brief Introduction
In the digital intelligence era, the rapid development of new technologies, particularly artificial intelligence generated content (AIGC), is rapidly changing our daily lives and business models. However, the inherent vulnerabilities in this emerging technology, along with insufficient safeguarding mechanisms and human errors, have raised consumer concerns about data privacy and security. The large models used as the foundation for AIGC are especially susceptible to data security risks. These risks arise from extensive data capture, automated transmission of interactive data, and customized training processes, potentially leading to compromised customer trust, regulatory penalties, and damage to brand reputation. Therefore, there is an urgent need for businesses to not only embrace AIGC for competitive advantage, but also to implement robust privacy and security measures to protect against emerging threats.
As a response to these challenges, this track aims to encourage cutting edge research that focuses on topics related to privacy and security in AIGC, such as developing strategies for identifying privacy and cybersecurity risks related to AIGC, proposing possible solutions to address privacy and security concerns while realizing the values generated by the technology. We welcome empirical (qualitative, quantitative, or mixed-methods) studies as well as design-oriented research and conceptual/theoretical papers for theory development.
Topics
1. Privacy and security in AIGC
2. Cybersecurity risk analysis and management
3. Employee accountability, insider threats, computer abuse, and employee insecure behaviors
4. Economic aspects of managing privacy and security
5. Tradeoffs between analytics initiatives and privacy/security concerns
6. Corporate strategies, governance, and compliance in privacy and cybersecurity
7. Socio-technical policies and mechanisms for countering cyber threats
8. Design and development of privacy and security enhancing technologies
9. Neuroscience applications to privacy and security
10. Privacy and security of mobile applications
11. Behavioral factors for privacy and security related user behaviors
12. Applying machine learning to enhance user privacy decisions