Who / When / Where
Instructor: Zhiyu Wan
Teaching Assistant: Sihan Xie, Hongzhu Jiang
Semester: Fall 2025
Time: Wednesdays & Fridays (Odd Week) , 15:00-15:45, 15:55-16:40
Location: School of Life Science and Technology, Room A103
Office Hours: Upon request, Location: BME Building, Room 228
Schedule
(Note: This schedule is tentative and subject to change.)
| Week | Chapter | Teaching Contents | Readings | Assignments |
| Week 1, Wednesday Sept 17, 15:00-16:40 Lecture 1 (PDF) | I. Course Introduction and Overview of Data Privacy and Ethics (a) | Course introduction. The role of medical data in the age of AI. Concept of data privacy and its importance. | / | / |
| Week 1, Friday Sept 19, 15:00-16:40 Lecture 2 (PDF) | I. Course Introduction and Overview of Data Privacy and Ethics (b) | Concept of ethics and morality and their importance. AI ethics: machine morality and ethics, automation, and employment. | (Optional: 1.《信息科学技术伦理与道德》Chs.8-10. 2.《工程伦理》Ch.12. 3.《信息科学技术伦理与道德》Chs.3&4.) | / |
| Week 2, Wednesday Sept 24, 15:00-16:40 Lecture 3 (PDF) | II. Ethical Issues in Life Sciences, Medicine, and Informatics (a) | Research ethics: ethical guidelines for human and animal experiments and scientific research. Life sciences ethics: controversies arising from reproductive technology, genetic technology, stem cell research, etc. Information security and privacy issues: personal data breaches, surveillance technology, facial recognition. | (Optional: 1.《工程伦理》Ch.10. 2.《信息科学技术伦理与道德》Chs.5&7.) | |
| Week 3, Wednesday Oct 1, 15:00-16:40 | National Day (No class) | / | / | / |
| Week 3, Friday Oct 3, 15:00-16:40 | National Day (No class) | / | / | / |
| Week 4, Saturday Oct 11, 15:00-16:40 Lecture 4 (PDF) | II. Ethical Issues in Life Sciences, Medicine, and Informatics (b) | Medical ethics issues: abortion, euthanasia, public health issues, and medical ethics concerning special populations (infants, adolescents, psychiatric patients, etc.). | 1. Murdoch B. Privacy and artificial intelligence: challenges for protecting health information in a new era. BMC medical ethics. 2021 Sep 15;22(1):122. https://link.springer.com/article/10.1186/s12910-021-00687-3 (Optional: 2. Price WN, Cohen IG. Privacy in the age of medical big data. Nature medicine. 2019 Jan;25(1):37-43. 3. S. Warren and L. Brandeis. The right to privacy. Harvard Law Review. 1890. http://faculty.uml.edu/sgallagher/Brandeisprivacy.htm) | |
| Week 5, Wednesday Oct 15, 15:00-16:40 Lecture 5 (PDF) | III. Ethical Issues in Data Sharing and Medical AI (a) | In-class quiz 1. Data ethics: from principle to practice. Data governance and data lifecycle management. Importance of biomedical data sharing and privacy challenges. | 1. Malin B, Benitez K, Masys D. Never too old for anonymity: a statistical standard for demographic data sharing via the HIPAA Privacy Rule. Journal of the American Medical Informatics Association. 2011 Jan 1;18(1):3-10. https://doi.org/10.1136/jamia.2010.004622 (Optional 2. McGraw D, Dempsey JX, Harris L, Goldman J. Privacy as an enabler, not an impediment: building trust into health information exchange. Health affairs. 2009 Mar;28(2):416-27. http://content.healthaffairs.org/content/28/2/416.full.pdf+html 3. U.S. Department of Health and Human Services. Summary of the Privacy Rule of the Health Information Portability and Accountability Act (HIPAA) http://www.hhs.gov/ocr/privacy/hipaa/understanding/summary/index.html) | |
| Week 5, Friday Oct 17, 15:00-16:40 Lecture 6 (PDF) | III. Ethical Issues in Data Sharing and Medical AI (b) | Algorithmic fairness and bias issues in medical AI. | (Optional: 1. Kearns M, Roth A. The ethical algorithm: The science of socially aware algorithm design. Oxford University Press; 2019 Oct 4. Ch.2. 2. 《Ethics of medical AI》pp. 117-132. 3. Dunkelau J, Leuschel M. Fairness-aware machine learning: An extensive overview. 2019. https://stups.hhu-hosting.de/downloads/pdf/fairness-survey.pdf) | |
| Week 6, Wednesday Oct 22, 15:00-16:40 Lecture 7 (PDF) | III. Ethical Issues in Data Sharing and Medical AI (c) | Transparency and interpretability in medical AI. | 1. Xu J, Xiao Y, Wang WH, Ning Y, Shenkman EA, Bian J, Wang F. Algorithmic fairness in computational medicine. EBioMedicine. 2022 Oct 1;84. https://www.thelancet.com/pdfs/journals/ebiom/PIIS2352-3964(22)00432-7.pdf (Optional: 2. Molnar, Christoph. Interpretable machine learning. 2020. (Ch. 5) https://christophm.github.io/interpretable-ml-book/ 3. Lundberg, S. M., & Lee, S. I. A unified approach to interpreting model predictions. NeurIPS. 2017 (Original SHAP paper).) | |
| Week 7, Wednesday Oct 29, 15:00-16:40 Lecture 8 (PDF) | IV. Legal and Regulatory Frameworks (a) | In-class quiz 2. The impact of international laws and regulations like HIPAA and GDPR on biomedical data. | (Optional: 1. Gruschka N, Mavroeidis V, Vishi K, Jensen M. Privacy issues and data protection in big data: a case study analysis under GDPR. In 2018 IEEE international conference on big data (big data) 2018 Dec 10 (pp. 5027-5033). IEEE.) | |
| Week 7, Friday Oct 31, 15:00-16:40 Lecture 9 (PDF) | IV. Legal and Regulatory Frameworks (b) | China’s data security law and personal information protection law. Compliance requirements for medical data sharing. | (Optional: 1. 《信息科学技术伦理与道德》Chs.5.2.) | |
| Week 8, Wednesday Nov 5, 15:00-16:40 Lecture 10 (PDF) | V. Privacy Risks & Common Privacy Protections of Biomedical Data (a) | Privacy risks of electronic health records. Main attack models: re-identification attacks. Privacy risk assessment methods. | 1. Sweeney L. Simple Demographics Often Identify People Uniquely. Carnegie Mellon University, Data Privacy Working Paper 3. Pittsburgh 2000. https://dataprivacylab.org/projects/identifiability/paper1.pdf (Optional: 2. Golle P. Revisiting the uniqueness of simple demographics in the US population. In Proceedings of the 5th ACM Workshop on Privacy in Electronic Society 2006 Oct 30 (pp. 77-80). https://crypto.stanford.edu/~pgolle/papers/census.pdf) | |
| Week 9, Wednesday Nov 12, 15:00-16:40 Lecture 11 (PDF) | V. Privacy Risks & Common Privacy Protections of Biomedical Data (b) | Privacy risks of natural language medical data. Privacy risks of medical image data. Case studies on breaches of different types of medical data. | N/A | |
| Week 9, Friday Nov 14, 15:00-16:40 Lecture 12 (PDF) | V. Privacy Risks & Common Privacy Protections of Biomedical Data (c) | In-class quiz 3. Classic data privacy protection techniques: pseudonymization, K-anonymization, and others. Announce course project topics. | 1. Sweeney L. k-anonymity: A model for protecting privacy. International journal of uncertainty, fuzziness and knowledge-based systems. 2002 Oct;10(05):557-70. https://epic.org/wp-content/uploads/privacy/reidentification/Sweeney_Article.pdf (Optional: 2. Dankar FK, El Emam K. A method for evaluating marketer re-identification risk. InProceedings of the 2010 EDBT/ICDT Workshops 2010 Mar 22 (pp. 1-10). 3. Newton EM, Sweeney L, Malin B. Preserving privacy by de-identifying face images. IEEE transactions on Knowledge and Data Engineering. 2005 Jan 10;17(2):232-43.) | HW #1 Posted. |
| Week 10, Wednesday Nov 19, 15:00-16:40 Lecture 13 (PDF) | V. Privacy Risks & Common Privacy Protections of Biomedical Data (d) | Introduction to Genomic sequencing and Genome-Wide Association Study (Guest Lecturer) | N/A | |
| Week 11, Wednesday Nov 26, 15:00-16:40 Lecture 14 (PDF) | V. Privacy Risks & Common Privacy Protections of Biomedical Data (e) | Sharing and privacy risks of genomic data. Main attack models: membership inference attacks, and reconstruction attacks. | 1. Sankararaman S, Obozinski G, Jordan MI, Halperin E. Genomic privacy and limits of individual detection in a pool. Nature genetics. 2009 Sep;41(9):965-7. (Optional: 2. Homer N, Szelinger S, Redman M, Duggan D, Tembe W, Muehling J, Pearson JV, Stephan DA, Nelson SF, Craig DW. Resolving individuals contributing trace amounts of DNA to highly complex mixtures using high-density SNP genotyping microarrays. PLoS genetics. 2008 Aug 29;4(8):e1000167. 3. 《Responsible Genomic Data Sharing Challenges and Approaches》Chs.1,3&4.) | |
| Week 11, Friday Nov 28, 15:00-16:40 Lecture 15 (PDF) | V. Privacy Risks & Common Privacy Protections of Biomedical Data (f) | Basics of game theory. Game-theoretic models for optimized privacy protection. Complete team formation. | 1. Wan Z, Vorobeychik Y, Xia W, Clayton EW, Kantarcioglu M, Malin B. Expanding access to large-scale genomic data while promoting privacy: a game theoretic approach. The American Journal of Human Genetics. 2017 Feb 2;100(2):316-22. https://www.cell.com/action/showPdf?pii=S0002-9297%2816%2930526-2 （Due on Dec 3) (Optional: 2. Wan Z, Vorobeychik Y, Xia W, Clayton EW, Kantarcioglu M, Ganta R, Heatherly R, Malin BA. A game theoretic framework for analyzing re-identification risk. PloS one. 2015 Mar 25;10(3):e0120592. https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0120592&type=printable 3. Wan Z, Vorobeychik Y, Xia W, Liu Y, Wooders M, Guo J, Yin Z, Clayton EW, Kantarcioglu M, Malin BA. Using game theory to thwart multistage privacy intrusions when sharing data. Science Advances. 2021 Dec 10;7(50):eabe9986. https://www.science.org/doi/pdf/10.1126/sciadv.abe9986) | |
| Week 12, Wednesday Dec 3, 15:00-16:40 Lecture 16 (PDF) | V. Privacy Risks & Common Privacy Protections of Biomedical Data (g) | Basic principles and applications of differential privacy. Access control and audit techniques. Complete project topic selection. | 1. Dwork C. Differential privacy. In International colloquium on automata, languages, and programming 2006 Jul 10 (pp. 1-12). Berlin, Heidelberg: Springer Berlin Heidelberg. https://www.comp.nus.edu.sg/~tankl/cs5322/readings/dwork.pdf (Due on Dec 10) (Optional: 2. Clifton C, Tassa T. On syntactic anonymity and differential privacy. Transactions on Data Privacy. 2013 Aug 22;6(2):161-83. http://www.tdp.cat/issues11/tdp.a124a13.pdf 3. Schlegelmilch J, Steffens U. Role mining with ORCA. In Proceedings of the tenth ACM symposium on Access control models and technologies 2005 Jun 1 (pp. 168-176). https://dl.acm.org/doi/pdf/10.1145/1063979.1064008) | HW #1 Due. |
| Week 13, Wednesday Dec 10, 15:00-16:40 Lecture 17 (PDF) | VI. Advanced Privacy Protections of Biomedical Data (a) | Basics of cryptography. Methods and applications of homomorphic encryption technologies for privacy-preserving computing in medicine. Methods and applications of secure multi-party computation technologies for privacy-preserving computing in medicine. Methods and applications of encrypted hardware technologies for privacy-preserving computing in medicine. | 1. Kantarcioglu M, Jiang W, Liu Y, Malin B. A cryptographic approach to securely share and query genomic sequences. IEEE Transactions on information technology in biomedicine. 2008 Sep 3;12(5):606-17. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=4358920 (Due on Dec 17) (Optional: 2. Jha S, Kruger L, Shmatikov V. Towards practical privacy for genomic computation. In 2008 IEEE Symposium on Security and Privacy (sp 2008) 2008 May 18 (pp. 216-230). IEEE. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=4531155 3.《Responsible Genomic Data Sharing Challenges and Approaches》Chs.5&6.) | Project Description (1 page) Due. |
| Week 13, Friday Dec 12, 15:00-16:40 Lecture 18 (PDF) | VI. Advanced Privacy Protections of Biomedical Data (b) | Methods and applications of federated learning models in privacy protection. Methods and applications of synthetic data generation techniques in privacy protection. | (Optional: 1. Li T, Sahu AK, Talwalkar A, Smith V. Federated learning: Challenges, methods, and future directions. IEEE signal processing magazine. 2020 May 1;37(3):50-60. https://doi.org/10.1109/MSP.2020.2975749 2. Yan C, Yan Y, Wan Z, Zhang Z, Omberg L, Guinney J, Mooney SD, Malin BA. A multifaceted benchmarking of synthetic electronic health record generation models. Nature communications. 2022 Dec 9;13(1):7609. https://doi.org/10.1038/s41467-022-35295-1 | HW #2 Posted |
| Week 14, Wednesday Dec 17, 15:00-16:40 Lecture 19 (PDF) | VI. Advanced Privacy Protections of Biomedical Data (c) | Presentations of team project proposals. | Project Proposal Presentation and Report Due at 23:59 on Dec 19. | |
| Week 15, Wednesday Dec 24, 15:00-16:40 Lecture 20 (PDF) | VII. Privacy and Ethical Issues in Cutting-Edge AI Technologies for Healthcare | Basics of large language models and generative AI. Applications of large language models and generative AI in medicine. Privacy risk issues of large language models and generative AI. Other ethical issues of large language models and generative AI. Solutions to ethical issues of large language models and generative AI. | 1. Wiest IC, Leßmann ME, Wolf F, Ferber D, Treeck MV, Zhu J, Ebert MP, Westphalen CB, Wermke M, Kather JN. Deidentifying Medical Documents with Local, Privacy-Preserving Large Language Models: The LLM-Anonymizer. NEJM AI. 2025 Mar 27;2(4):AIdbp2400537. https://ai.nejm.org/doi/pdf/10.1056/AIdbp2400537 (Optional: 2. Das BC, Amini MH, Wu Y. Security and privacy challenges of large language models: A survey. ACM Computing Surveys. 2025 Feb 10;57(6):1-39.) | |
| Week 15, Friday Dec 26, 15:00-16:40 | Final Project Presentation Due at 23:59 on Dec 26. | |||
| VIII. Course Project Presentation (a) | Group presentations and feedback. | |||
| Week 16, Wednesday Dec 31, 15:00-16:40 | VIII. Course Project Presentation (b) | Group presentations and feedback. | HW #2 Due at 15:00 on Jan 7. Final Project Report (Research Paper) Due at 23:59 on Jan 9. |
Zhiyu Wan 