AI Privacy & Governance

Research Publications

Lim, D. (2024). Innovation and Artists' Rights in the Age of Generative AI. Georgetown Journal of International Affairs (forthcoming). 

Wu, C., Zhang, H., & Carroll, J. M. (2024). AI Governance in Higher Education: Case Studies of Guidance at Big Ten Universities. arXiv preprint arXiv:2409.02017.​​​​​

Mosqueda González, B. A., Hasan, O., Uriawan, W., Badr, Y., & Brunie, L. (2023). Secure and efficient decentralized machine learning through group-based model aggregation. Cluster Computing, 1–15.

Sangwan, R. S., Badr, Y., & Srinivasan, S. M. (2023). Cybersecurity for AI Systems: A Survey. Journal of Cybersecurity and Privacy, 3(2), Article 2. 

Xie, J., Yu, R., Zhang, H., Lee, S., Billah, S. M., & Carroll, J. M. (2024). BubbleCam: Engaging Privacy in Remote Sighted Assistance. Proceedings of the CHI Conference on Human Factors in Computing Systems, 1–16. 

Cao, B., Li, C., Wang, T., Jia, J., Li, B., & Chen, J. (2023). IMPRESS: Evaluating the Resilience of Imperceptible Perturbations Against Unauthorized Data Usage in Diffusion-Based Generative AI. Advances in Neural Information Processing Systems, 36, 10657–10677.

Yin, Z., Ye, M., Zhang, T., Du, T., Zhu, J., Liu, H., Chen, J., Wang, T., & Ma, F. (2024). VLATTACK: Multimodal Adversarial Attacks on Vision-Language Tasks via Pre-trained Models (arXiv:2310.04655). arXiv. 

Zhang, H., Guo, Z., Zhu, H., Cao, B., Lin, L., Jia, J., Chen, J., & Wu, D. (2023). On the Safety of Open-Sourced Large Language Models: Does Alignment Really Prevent Them From Being Misused? (arXiv:2310.01581). arXiv. 

Ye, M., Chen, J., Miao, C., Liu, H., Wang, T., & Ma, F. (2023). PAT: Geometry-Aware Hard-Label Black-Box Adversarial Attacks on Text. Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 3093–3104. 

Vyas, S. D., Kumar Padisala, S., & Dey, S. (2023). A Physics-Informed Neural Network Approach Towards Cyber Attack Detection in Vehicle Platoons. 2023 American Control Conference (ACC), 4537–4542. 

Xiao, Y., Du, J., Zhang, S., Yan, Q., Zhang, D., & Kifer, D. (2024). Click Without Compromise: Online Advertising Measurement via Per User Differential Privacy (arXiv:2406.02463). arXiv. 

Li, X., Chen, L., & Wu, D. (2023). Adversary for Social Good: Leveraging Adversarial Attacks to Protect Personal Attribute Privacy. ACM Trans. Knowl. Discov. Data, 18(2), 46:1-46:24. 

Xiong, A., Wu, C., Wang, T., Proctor, R. W., Blocki, J., Li, N., & Jha, S. (2022). Using Illustrations to Communicate Differential Privacy Trust Models: An Investigation of Users’ Comprehension, Perception, and Data Sharing Decision (arXiv:2202.10014). arXiv. 

Acquisti, A., Adjerid, I., Balebako, R., Brandimarte, L., Cranor, L. F., Komanduri, S., Leon, P. G., Sadeh, N., Schaub, F., Sleeper, M., Wang, Y., & Wilson, S. (2017). Nudges for privacy and security: Understanding and assisting users’ choices online. ACM Computing Surveys, 50(3).

Badr, Y., Zhu, X., & Alraja, M. N. (2021). Security and privacy in the Internet of Things: threats and challenges. Service Oriented Computing and Applications, 15(4), 257-271.

Dai, E., Zhao, T., Zhu, H., Xu, J., Guo, Z., Liu, H., Tang, J., & Wang, S. (2022). A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability (arXiv:2204.08570). arXiv.

Li, X., Chen, L., Zhang, J., Larus, J., & Wu, D. (2021). Watermarking-based Defense against Adversarial Attacks on Deep Neural Networks. 2021 International Joint Conference on Neural Networks (IJCNN), 1–8.

Li, Y., Yang, D., & Hu, X. (2020). A differential privacy-based privacy-preserving data publishing algorithm for transit smart card data. Transportation Research Part C: Emerging Technologies, 115, 102634.

Pridmore, J., Zimmer, M., Vitak, J., Mols, A., Trottier, D., Kumar, P. C., & Liao, Y. (2019). Intelligent personal assistants and the intercultural negotiations of dataveillance in platformed households. Surveillance & Society. 17(1/2). 125-131

Rajtmajer, S., & Susser, D. (2020). Automated influence and the challenge of cognitive security. Proceedings of the 7th Symposium on Hot Topics in the Science of Security, 1–9.

Ravichander, A., Black, A. W., Norton, T., Wilson, S., & Sadeh, N. (2021). Breaking Down Walls of Text: How Can NLP Benefit Consumer Privacy? Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), 4125–4140.

Swaminathan, S., Fok, R., Chen, F., Huang, T. H., Lin, I., Jadvani, R., ... & Bigham, J. P. (2017). Wearmail: On-the-go access to information in your email with a privacy-preserving human computation workflow. In Proceedings of the 30th Annual ACM Symposium on User Interface Software and Technology (pp. 807-815).

Xiong, A., Wang, T., Li, N., & Jha, S. (2020). Towards Effective Differential Privacy Communication for Users’ Data Sharing Decision and Comprehension. 2020 IEEE Symposium on Security and Privacy (SP), 392–410.

Hacker, P., Engel, A., & Mauer, M. (2023). Regulating ChatGPT and other Large Generative AI Models (arXiv:2302.02337). arXiv.

Helberger, N., & Diakopoulos, N. (2023). ChatGPT and the AI Act. Internet Policy Review, 12(1).

Copyright and Artificial Creation: Does EU Copyright Law Protect AI-Assisted Output? | SpringerLink. (n.d.). 

Torkzadehmahani, R., Nasirigerdeh, R., Blumenthal, D. B., Kacprowski, T., List, M., Matschinske, J., Spaeth, J., Wenke, N. K., & Baumbach, J. (2022). Privacy-Preserving Artificial Intelligence Techniques in Biomedicine. Methods of Information in Medicine.

Škiljić, A. (2021). When Art Meets Technology or Vice Versa: Key Challenges at the Crossroads of AI-Generated Artworks and Copyright Law. IIC - International Review of Intellectual Property and Competition Law, 52(10), 1338–1369.  

Svedman, M. (2020). Artificial Creativity: A Case Against Copyright for AI-Created Visual Artwork. IP Theory, 9(1).  

Hao, M., Li, H., Luo, X., Xu, G., Yang, H., & Liu, S. (2020). Efficient and Privacy-Enhanced Federated Learning for Industrial Artificial Intelligence. IEEE Transactions on Industrial Informatics, 16(10), 6532–6542.

Theodorou, A., & Dignum, V. (2020). Towards ethical and socio-legal governance in AI. Nature Machine Intelligence, 2(1), 10–12.

Schiff, D., Biddle, J., Borenstein, J., & Laas, K. (2020). What’s Next for AI Ethics, Policy, and Governance? A Global Overview. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 153–158.

Perry, B., & Uuk, R. (2019). AI Governance and the Policymaking Process: Key Considerations for Reducing AI Risk. Big Data and Cognitive Computing, 3(2), 26.

Medsker, L. (2019). AI policy matters. AI Matters, 4(4), 16–18.

Yeung, K., Howes, A., & Pogrebna, G. (2019). AI Governance by Human Rights-Centred Design, Deliberation and Oversight: An End to Ethics Washing. SSRN Electronic Journal.

Young, M., Rodriguez, L., Keller, E., Sun, F., Sa, B., Whittington, J., & Howe, B. (2019). Beyond Open vs. Closed: Balancing Individual Privacy and Public Accountability in Data Sharing. Proceedings of the Conference on Fairness, Accountability, and Transparency, 191–200.

Zhu, T., & Yu, P. S. (2019). Applying Differential Privacy Mechanism in Artificial Intelligence. 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), 1601–1609.

Cath, C. (2018). Governing artificial intelligence: Ethical, legal and technical opportunities and challenges. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2133), 20180080.

Governing artificial intelligence: Ethical, legal and technical opportunities and challenges | Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. 

Siau, K., & Wang, W. (2018). Artificial Intelligence: A Study on Governance, Policies, and Regulations.

Stahl, B. C., & Wright, D. (2018). Ethics and Privacy in AI and Big Data: Implementing Responsible Research and Innovation. IEEE Security & Privacy, 16(3), 26–33.

Villaronga, E. F., Kieseberg, P., & Li, T. (2018). Humans forget, machines remember: Artificial intelligence and the Right to Be Forgotten. Computer Law & Security Review, 34(2), 304–313.

Garvey, C. (2018). AI Risk Mitigation Through Democratic Governance: Introducing the 7-Dimensional AI Risk Horizon. Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, 366–367.

Gruetzemacher, R. (2018). Rethinking AI Strategy and Policy as Entangled Super Wicked Problems. Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, 122–122.

Li, T., Villaronga, E. F., & Kieseberg, P. (2017). Humans Forget, Machines Remember: Artificial Intelligence and the Right to Be Forgotten. 20.

Coglianese, C., & Lehr, D. (2017). Regulating by Robot: Administrative Decision Making in the Machine-Learning Era. THE GEORGETOWN LAW JOURNAL, 105.

Li, X., & Zhang, T. (2017). An exploration on artificial intelligence application: From security, privacy and ethic perspective. 2017 IEEE 2nd International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), 416–420. 

Nissan, E. (2017). Digital technologies and artificial intelligence’s present and foreseeable impact on lawyering, judging, policing and law enforcement. AI & SOCIETY, 32(3), 441–464.

West, D. M., & Allen, J. R. (2020). Turning point: Policymaking in the era of artificial intelligence. Brookings Institution Press.   

Ammanath, B. (2022). Trustworthy AI: A Business Guide for Navigating Trust and Ethics in AI. John Wiley & Sons. 

Georghiou, A. (2020). AI: My Story; The Story AI Tells; Bias & Privacy. Life Betterment Through God, LLC. 

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