INSE6180 Privacy in Data Mining (Summer 2020)

Instructor: Jia Yuan Yu.

Lectures: Mondays and Wednesdays, 17:45--20:15, online.

Office hours: Fridays, 15:45--17:45, online.

Course content: Introduction to data mining and its applications; (projects) privacy legislation security and privacy threats caused by current data mining techniques; (projects) risks and challenges in emerging data mining applications; (projects) attacks and prevention methods: web privacy attacks, data mining-based intrusion detection; privacy-preserving data mining; privacy-preserving data publishing.

References:

• The Elements of Statistical Learning, Hastie, Tibshirani, and Friedman.
• The Algorithmic Foundations of Differential Privacy, Dwork and Roth.

Announcements

Lecture notes

• Notes from my previous Machine Learning course.
• Notes on Privacy in Data Mining [PDF].

Projects

In the spirit of the subject of this course, each student will work on an individual project. The output of theresearch project will be a report and a presentation. In the project, you must find an open dataset, analyzethe privacy vulnerability of that dataset, and propose how to prevent attacks. Bonus marks for the project that finds the most vulnerable dataset, and the best prevention solution.

You will have to produce a concise report (neither too short nor too long), and give a presentation on it.