All programmes > Privacy and Big Data

Privacy and Big Data (B-KUL-H00Y2A)

4 ECTSEnglish30 First termCannot be taken as part of an examination contract
Gálvez Vizcaíno Rafa |  N. |  Gálvez Vizcaíno Rafa (substitute)
POC Artificial Intelligence

Aims

The students understand the privacy risks associated to big data analysis. 

The students are familiar with privacy preserving techniques relevant to big data. They understand the basic principles of these technologies, as well as their limitations, and are able to apply them in practical scenarios.

The students understand the basic legal and ethical principles that are relevant when dealing with big data.

The students are able to perform a privacy impact assessment of an application or service. They can identify privacy concerns from a technical, legal and ethical perspective and they can propose legal, technical and organizational measures for mitigating those concerns. 

Previous knowledge

Basic knowledge of information and communication systems. Knowledge of cryptography, computer and network security is useful but not essential.

Identical courses

This course is identical to the following courses:
H00Y2B : Privacy and Big Data

Is included in these courses of study

Activities

3 ects. Privacy and Big Data: Lecture (B-KUL-H00Y2a)

3 ECTSEnglishFormat: Lecture20 First term
N. |  Gálvez Vizcaíno Rafa (substitute)
POC Artificial Intelligence

Content

The course covers the following topics:

  • Introduction to computer privacy and privacy engineering
  • Database anonymity and privacy: k-anonymity, l-diversity, t-closeness, re-identification attacks, statistical disclosure control, differential privacy
  • Privacy by design
  • Privacy and machine learning
  • Privacy risk analysis
  • Web privacy and user tracking
  • General Data Protection Regulation (GDPR), human rights legislation, relevant policy for Big Data, data protection impact assessments
  • Ethical issues of Big Data, Discrimination-aware data-mining, algorithmic accountability

 

Course material

Slides, courseware, articles and literature

Is also included in other courses

1 ects. Privacy and Big Data: Practical Sessions (B-KUL-H00Y3a)

1 ECTSEnglishFormat: Practical10 First term
Gálvez Vizcaíno Rafa
POC Artificial Intelligence

Content

The first two practical sessions will be devoted to group discussions and feedback on drafts of the assignment. Students work together on the assignment in teams of 2 or 3 people (see evaluation for more details). Students working together on the same team should be in different groups during the exercise session discussions, in order to maximize the feedback obtained from students in other groups. 

In the first session students will discuss their initial ideas for the assignment (description of their chosen application and initial identification of privacy issues).

In the second session students will discuss a more complete draft of their assignment and get a second round of feedback for further improvement.

The last two practical sessions will be devoted to presentations of the assignments by the students. The presentations will be graded (4 points out of 20). Students get feedback on their presentation that they can incorporate in the final version of the assignment. 

Course material

Slides, courseware, articles and literature

Is also included in other courses

Evaluation

Evaluation: Privacy and Big Data (B-KUL-H20Y2a)

Type : Continuous assessment without exam during the examination period
Description of evaluation : Paper/Project, Presentation
Type of questions : Open questions
Learning material : None

Explanation


Students will select (in teams of 2 or 3 people) a case study in the second week of the class. The case study is an application of service that utilizes Big Data.

Students must perform a privacy impact assessment, meaning in-depth analysis of the case study with respect to the privacy (legal, technical, and ethical) aspects covered in the class, in an assignment of between 3500 and 4500 words (+/- 15 pages). The assignment text includes: description of the application, analysis of privacy issues and proposed recommendations to address those issues. 

The teams present their work in a short presentation (5-10 minutes, depending on number of teams) followed by some questions and feedback. The text of the assignment is due after the presentation sessions and before the start of the examination period. 

The presentation is graded with 4 points and the final text of the assignment with 16 points. 

Information about retaking exams

In the second examination period all 20 points are evaluated on the basis of the written assignment (no presentation). Assignments must be submitted BEFORE the start of the examination period (deadline is the last day before the examnination period starts).