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. 

LEARNING OUTCOMES

Students will learn key elements in data protection and privacy: data privacy and anonymity, metrics and techniques; macro and microdata protection; data protection in outsourcing scenarios; privacy on the web; advanced access control.

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 

• Privacy Regulations 

RECOMMENDED READING/BIBLIOGRAPHY

Scientific papers and slides that will be provided during the course

Exam Board

ALESSIO MERLO (President)

MARCO MARATEA

ALESSANDRO ARMANDO

Teaching methods

lectures + hands on

LESSONS START

October 2018

Exam description

Oral examination

Assessment methods

Pitch and demo of a project