Security and Privacy
AbstractThis 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.
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.
Before taking this course you must:
- Know basic algorithms and data structures
- Have implemented at least two data analysis projects
- Be able to design, implement, and test medium-sized programs in Java or Python or other mainstream languages.
- Be familiar with basic discrete mathematics
- Be familiar with basic probability theory and statistics
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.
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.
To be eligible for the examination, you must:
- Submit and have approved 2 mandatory exercise sets
- Submit and have approved 2 mini-projects.
- 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.
The course literature is published in the course page in LearnIT.
Student Activity BudgetEstimated 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 examExam type:
C: Submission of written work, External (7-point scale)
C22: Submission of written work – Take home
Duration: 4 hours (please disregard the 1 day duration below).
Lecture videos, slides, readings and further online and offline materials.
No communication and collaboration allowed.
Random fraud control with Zoom.
Will be conducted right after the submission.
Student Affairs and Programmes will randomly select 20 % of students who will have to show up in Zoom to check authorship of submitted solutions.
The selection of students for fraud control will be published in LearnIT right after the exam together with a link to the Zoom meeting.
More information in LearnIT before the exam.