Privacy and Data Protection

Privacy and Data Protection

Academic year 2020/2021

Sommario del corso

• Course objectives
• Course delivery
• Program
• Teaching tools

Course objectives

The course aims to introduce the main methods for privacy-preserving management and analysis of the so-called "big data". This part focuses on the main anonymization methods and privacy-preserving computation models proposed so-far, with particular attention to the challenges of the so-called "big data era".

Course delivery

5 lessons (Computer Science Department - Room B):

1. November 17th, 2020 (9.00 - 11.00)
2. November 18th, 2020 (9.00 - 13.00)
3. November 24th, 2020 (9.00 - 13.00)
4. November 25th, 2020 (9.00 - 13.00)
5. December 1st, 2020 (9.00 - 13.00)
6. December 2nd, 2020 (9.00 - 11.00)

Program

1. The concept of privacy and privacy laws in different countries
2. The challenges of privacy in the "Big Data Era"
3. Information systems and privacy
4. Privacy attacks and models in statistical databases
   1. k-anonymity
   2. l-diversity
   3. t-closeness
   4. delta-presence
5. Privacy-preserving data analytics
   1. randomization
   2. secure multiparty computation
6. Differential privacy
   1. Laplace mechanism
   2. Exponential mechanism

Class schedule

• Teaching materials
• Exams and finals
• Go to Moodle
• Visit the forums