Security and Privacy (Autumn 2019)

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.

Learning activities

14 weeks of teaching consisting of lectures and exercises. Coursework takes the following forms. 

• Lectures introducing & discussing concepts. 
• Assignments: Exercises sets
• Assignments: Project work 

The exercise sets will provide examples and activities for the concepts introduced and discussed during the lectures. The concepts will be furthermore applied in mini-projects through the course and in the course final project. More details regarding the assignments will be posted on LearnIT.

Mandatory activities

To be eligible for the examination, you must:  

1. Submit and have approved 2 mandatory exercise sets  
   
2. Submit and have approved 2 mini-projects.  
   
3. Participate actively in the course final project

All deadlines will be announced on the course page on LearnIT.

The student will receive the grade NA (not approved) at the ordinary exam, if the mandatory activities are not approved and the student will use an exam attempt.

Course literature

The course literature is published in the course page in LearnIT.

Ordinary exam

Exam type:
A: Written exam on premises, external (7-trinsskala)
Exam variation:
A11: Written exam on premises. Open book exam. You are allowed to use all aids (books, notes, software and online resources).
Exam description:

Duration: 4 hours

reexam

Exam type:
Z. To be decided, - (-)

Time and date