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