1. ITIS 6220/8220 Data Privacy Fall 2012

2. Overview Class hour 6:30 – 9:15pm, Monday Office hour 4pm – 6pm, Monday Instructor - Dr. Xintao Wu email - xwu@uncc.eduxwu@uncc.edu Office – Woodward Hall 333E Webpage http://www.sis.uncc.edu/~xwu/6220/6220.htm Textbook Privacy-Preserving Data Mining: Models and Algorithms by Charu C. Aggarwal and Philip S. Yu, Springer, 2008. ISBN: 978-0-387- 70991-8 (recommended) Online information, http://www.springer.com/computer/security+and+cryptology/book /978-0-387-70991-8

3. Topic Description Privacy law, policy, regulations Introduction to privacy preserving data mining K-anonymity vs. l-diversity Randomization methods Transformation based perturbation Distributed privacy preserving data mining Privacy issues in various application domains social networks finance, medical domains location based systems, clouds Privacy in statistical databases Inference control methods Differential privacy

4. Course Prerequisite ITIS 62006200 Knowledge of basic statistics, data mining, and database. Linear algebra is not required but very helpful

5. Grading Composition Projectand presentation 40% Midterm 25% Final 35% Scale >=90% = A 80-89% = B 70-79% = C <70% or cheating = F

6. Project reports Late policy: No acceptable. Must be word or pdf format. Electronic submit accepted

7. Project/Research papers Develop a privacy preserving data mining system by implementing ppdm algorithms For a specific domain For some specific tasks Each group with 3-4 students Or develop attacking methods (e.g., de- anonymization) Write a research paper (doctoral student) Survey paper is discouraged More information http://www.sis.uncc.edu/~xwu/6220/proj.htm

8. Final Open books and notes No discussion Cumulative No makeup Class attendance is not required Bonus is expected