OVERVIEW

The course will introduce methodologies at the state of the art for protecting several data types (e.g., databases, time series, graphs, longitudinal data and transactional data). Furthermore, the course will provide some insights on legal aspects related to the protection of the user's privacy. 

AIMS AND CONTENT

LEARNING OUTCOMES

Students will learn the theoretical and practical bases of the anonymization of personal data. In particular, students will study state-of-the-art techniques for the anonymization of multidimensional data, graphs, time series, longitudinal and transactional data, as well as some legal bases on the protection of personal data.

AIMS AND LEARNING OUTCOMES

• understand the data privacy problem
• learn data anonymization algorithms at the stare of the art
• read and understand a scientific paper
• implement an anonymization technique, autonomously.

PREREQUISITES

• Programming
• Foundations of Algorithms and Data Structures
• Algebraic and statistical foundations.

TEACHING METHODS

lectures and hands on (60% - 40%)

SYLLABUS/CONTENT

• Introduction to data anonymization
• Algoritmi di anonimizzazione di:
  • multidimensional data: k-anonymity, l-diversity, t-cloesenss
  • graphs: k-degree anonymity
  • time series: (k,p)-anonymity
  • longitudinal data: UGACLIP
  • transactional data: CAHD
• Threats to Data Anonymization: Privacy Skyline
• Privacy-preserving data mining: MASK
• Privacy-preserving Test Data Manufacturing: kb-anonymity
• Deanonymizing bitcoins

RECOMMENDED READING/BIBLIOGRAPHY

Scientific papers and slides that will be provided during the course

TEACHERS AND EXAM BOARD

Exam Board

ALESSIO MERLO (President)

LUCA VERDERAME (President Substitute)

FRANCESCO PAGANO (Substitute)

LESSONS

LESSONS START

October 2018

EXAMS

EXAM DESCRIPTION

Oral examination

ASSESSMENT METHODS

Pitch and demo of a project

Exam schedule