Privacy Enhancing Technologies
Instance: 2020/2021 - 1S
Cycles of Study/Courses
Teaching Staff - Responsibilities
Teaching - Hours
|Theoretical and practical :
Last updated on 2020-10-01.
Fields changed: Teaching methods and learning activities, Componentes de Avaliação e Ocupação, Programa, Fórmula de cálculo da classificação final
Suitable for English-speaking students
The course aims to introduce students to the concepts and problems of data privacy and privacy enhancing technologies. Throughout the course, the students will review the fundamental concepts of security and privacy, followed by anonymization techniques for privacy-preserving data publishing and assessment of re-identification risks. It will also address the topics of secure multiparty computation and its application for privacy preserving data mining, anonymous communications and authentication.
Learning outcomes and competences
At the end of this course, the students must:
• Recognize threats to privacy
• Explain the terminology and basic concepts of privacy and use them correctly
• Have a general grasp of existing technologies for privacy reinforcement (PETs)
• Understand the specifications of PET systems in terms of the protection they provide and the way they work
• Identify vulnerabilities in the specifications of systems and predict impending threats
• Select countermeasures for the identified threats and estimate their efficacy
• Compare countermeasures and evaluate their collateral effects
• Present and explain their rationale to others
Pre-requirements (prior knowledge) and co-requirements (common knowledge)
Security and privacy concepts
Privacy-preserving data publishing: data anonymization, re-identification attacks
Secure multiparty computation and privacy
William Stallings; Information Privacy Engineering and Privacy by Design, Pearson, 2020. ISBN: 9780135278444
William Stallings; Computer security
. ISBN: 978-0-13-600424-0
Benjamin C. M. Fung, Ke Wang, Ada Wai-Chee Fu, and Philip S. Yu; Introduction to Privacy-Preserving Data Publishing Concepts and Techniques, Chapman & Hall/CRC, 2011
Stamp Mark; Information security
. ISBN: 9780470626399 hbk
Comments from the literature
Seminal and actual articles on privacy-enhancing technologies will also be used as reference.
Teaching methods and learning activities
In the lectures the essential concepts for understanding privacy enhancing technologies will be presented. Applications implementing the addressed methodologies will be used, particularly to assess the privacy-utility trade-offs of the presented privacy solutions. The course unit will have 2 assignments study and application of privacy-enhancing technologies. These works shall be complemented with case-studies for discussion and comprehension of privacy concepts.
Distributed evaluation with final exam
|Apresentação/discussão de um trabalho científico
|Trabalho prático ou de projeto
Amount of time allocated to each course unit
|Frequência das aulas
Eligibility for exams
- Minimum grade of 35% on lab assignments
- Minimum grade of 35% in exam
Note: both minimums should be met.
Class attendance is not recorded.
Calculation formula of final grade
Final grade will be based on assignment grade and exam grade, using the following expression: (AG x 10 + EG x 8 + CSG x 2) / 20
- EG = exam grade (0-20)
- AG = assignment grade (0-20)
- First assignment: 6 values
- Second assignment: 4 values
- CSG = case-studies grade (0-20)
This applies for all the exams (normal, recourse, grade improvement or special term)
Special assessment (TE, DA, ...)
Students wishing to improve their grades from the previous year may do so to both components by re-doing the one(s) they wish to improve.
This weighting applies to all exams: normal, "recurso", special season, last year student or grade improvement.
See grade calculation above.