Security and Privacy (Autumn 2024)

Abstract

This is an introductory course on information security and privacy for data science. The course focuses on aspects of principles and techniques of protecting the security and privacy of data that is collected for data analysis.

Description

The necessity of collecting and storing large amounts of data for analysis purposes raises critical issues of securing the collected data as well as protecting the privacy of its owners. The student taking this course will have an introductory knowledge on attacker models, cryptographic tools and principles of information security, as well as on methods for  data anonymisation and general understanding of privacy issues in data analysis. 

The course addresses the following topics: 

• The principal security requirements and attacker models 
• The fundamental cryptographic tools in information security
• Practical information security techniques for data protection
• Techniques and metrics for privacy-preserving data analysis
• Further challenges in security and data privacy from legal, societal and human factor perspective.

Formal prerequisites

Before taking this course you must:  

1. Know basic algorithms and data structures
2. Have implemented at least two data analysis projects
3. Be able to design, implement, and test medium-sized programs in Java or Python or other mainstream languages.
4. Be familiar with basic discrete mathematics
5. Be familiar with basic probability theory and statistics

Moreover the student must always meet the admission requirements of the IT University. Third year students in the Bachelor of Science in Data Science program should fulfil these requirements.

Intended learning outcomes

After the course, the student should be able to:

• Describe, relate, and discuss basic security principles
• Identify and describe access control techniques
• Identify and describe the proper use of cryptography in security and privacy
• Identify and describe the common principles for data privacy protection
• Describe, relate and apply different techniques for data anonymisation and evaluate their effectiveness
• Describe, related and discuss common challenges in data security and privacy from legal, societal and human factor perspectives.

Ordinary exam

Exam type:
A: Written exam on premises, External (7-point scale)
Exam variation:
A33: Written exam on premises on paper with restrictions