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

- To understand the data privacy problem

- To learn data anonymization algorithms at the stare of the art

- To read and understand a scientific paper

- To implement an anonymization technique, autonomously.

PREREQUISITES

- Programming

- Foundations of Algorithms and Data Structures

- Algebraic and statistical foundations.

TEACHING METHODS

lectures + hands on

SYLLABUS/CONTENT

- Static Data Anonymization for  Multidimensional Data and Complex Data Structures 

- Threats to Anonymized data

- Privacy-preserving Test Data Manufacturing

- Synthetic data generation

- Dynamic Data Protection: Tokenization

- Privacy through Data Hiding 

- Basics of Privacy Regulations 

RECOMMENDED READING/BIBLIOGRAPHY

Scientific papers and slides that will be provided during the course