Security and Privacy (Autumn 2023)

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

The final project is independent from the exam, as such it will not be graded, but the participation (consisting of submission of results + presentation) is a prerequisite for being allowed to the exam. 

The purpose of the mandatory activities is to give the students the opportunity to practice the knowledge and skills taught in the course in a timely fashion. The benefit of this is to reduce the stress in the exam preparation period by avoiding a concentration of obligations in the end of the semester, as well as ensuring that the students do the assignments in roughly the same time frame, so that they are able to help each other and get appropriate help from TAs/teachers (which are no longer available after the end of the 14-week semester). The students will be provided feedback on activities by TAs (in form of grading the activities as well as providing feedback during the exercise sessions if needed) and by the peers via comments following the final project presentation.

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

If a MA is missed or not approved, a make-up opportunity will be offered to ensure that the activity is completed and approved before the exam.

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.

Student Activity Budget

Estimated distribution of learning activities for the typical student

• Preparation for lectures and exercises: 25%
• Lectures: 25%
• Exercises: 10%
• Assignments: 15%
• Project work, supervision included: 15%
• Exam with preparation: 10%

Ordinary exam

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

reexam

Exam type:
B: Oral exam, External (7-point scale)
Exam variation:
B22: Oral exam with no time for preparation.

Time and date

Ordinary Exam - on premises Thu, 4 Jan 2024, 09:00 - 13:00