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Privacy Enhancing Technologies

Code: CC4068     Acronym: CC4068     Level: 400

Classification Keyword
OFICIAL Computer Science

Instance: 2020/2021 - 1S Ícone do Moodle

Active? Yes
Responsible unit: Department of Computer Science
Course/CS Responsible: Master in Information Security

Cycles of Study/Courses

Acronym No. of Students Study Plan Curricular Years Credits UCN Credits ECTS Contact hours Total Time
MI:ERS 11 Plano Oficial desde ano letivo 2014 4 - 6 42 162
M:SI 11 Study plan since 2020/2021 1 - 6 42 162

Teaching Staff - Responsibilities

Teacher Responsibility
João Paulo da Silva Machado Garcia Vilela

Teaching - Hours

Theoretical and practical : 3,00
Type Teacher Classes Hour
Theoretical and practical Totals 1 3,00
João Paulo da Silva Machado Garcia Vilela 3,00
Mais informaçõesLast updated on 2020-10-01.

Fields changed: Teaching methods and learning activities, Componentes de Avaliação e Ocupação, Programa, Fórmula de cálculo da classificação final

Teaching language

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

Working method


Pre-requirements (prior knowledge) and co-requirements (common knowledge)

  • Cryptography


Security and privacy concepts

Privacy-preserving data publishing: data anonymization, re-identification attacks

Location privacy

Secure multiparty computation and privacy

Private communications

Anonymous authentication

Mandatory literature

William Stallings; Information Privacy Engineering and Privacy by Design, Pearson, 2020. ISBN: 9780135278444
William Stallings; Computer security. ISBN: 978-0-13-600424-0

Complementary Bibliography

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.

Evaluation Type

Distributed evaluation with final exam

Assessment Components

designation Weight (%)
Exame 40,00
Apresentação/discussão de um trabalho científico 10,00
Trabalho prático ou de projeto 50,00
Total: 100,00

Amount of time allocated to each course unit

designation Time (hours)
Estudo autónomo 42,00
Frequência das aulas 39,00
Trabalho laboratorial 99,00
Total: 180,00

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.

Classification improvement

See grade calculation above.
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