Hasso-Plattner-InstitutSDG am HPI
Hasso-Plattner-InstitutDSG am HPI

Topics in Data Privacy (Wintersemester 2019/2020)

Dozent: Dr. Anne Kayem (Internet-Technologien und -Systeme)

Allgemeine Information

  • Semesterwochenstunden: 2
  • ECTS: 3
  • Benotet: Ja
  • Einschreibefrist: 01.10.-30.10.2019
  • Lehrform: Vorlesung / Übung
  • Belegungsart: Wahlpflichtmodul
  • Lehrsprache: Englisch
  • Maximale Teilnehmerzahl: 25

Studiengänge & Module

Cybersecurity MA
Data Engineering MA


The course is aimed at students with an interest in learning about methods of transforming data to protect against sensitive data exposure without having to resort to encrypting the data. As such these methods are centered on techniques that transform the data to eliminate personal identification information, for data protection, while maintaining data integrity and consistency to support data processing and/or sharing operations. The tendency is to assume that the GDPR legislation is the reason for the importance of this topic, however the question of how to share or make data available for use in a privacy preserving manner, has been at the crux of research on data privacy. Applications emerge in almost every domain involving sensitive data processing, but ones students might relate to include data analytics in the healthcare, cyber-physicial systems (e.g. smart homes, ...), image processing, and service/content personalisation domains.

The goal of the course is to bring the students abreast with the state-of-the-art on methods of transforming data to enable privacy preserving data analytics. As such, throughout this course, we will discuss various algorithms and architectures for data privacy focusing on their strengths in terms of privacy preservation, and weaknesses in terms of their vulnerabilities to sensitive data exposure; relating this to global legislature on the subject of data privacy. Topics covered will include: data anonymisation, identifiability of data, record linkage, de-identification, privacy preserving data analytics; and data protection legislation.


Block 1: Basic Principles of Data Privacy

Block 2: Syntactic Data Anonymisation

  • Lecture 3: (29.10.2019) - No Lecture (Jubilee Celebrations)
  • Lecture 4: (05.11.2019) - Anonymisation & De-Identification Techniques (I)
  • Lecture 5: (12.11.2019) - Anonymisation & De-Identification Techniques (II)

Block 3: Semantic Data Anonymisation

  • Lecture 6: (19.11.2019) - Differential Privacy
  • Lecture 7: (26.11.2019) - De-identification of Differentially Private Data (I)
  • Lecture 8: (03.12.2019) - De-identification of Differentially Private Data (II)
  • Lecture 9: (10.12.2019) - Comparing Privacy Solutions

Block 4: Data Privacy and the Web

  • Lecture 10: (07.01.2020) - Locating Personally Identifible Information; Student Presentations
  • Lecture 11: (14.01.2020) - Privacy Preserving Data Analytics; Student Presentations
  • Lecture 12: (21.01.2020) - Balancing Personalisation and Privacy; Student Presentations
  • Lecture 13: (28.01.2020) - Anonymity on Social Media; Student Presentations
  • Lecture 14: (04.02.2020) - Technology, Policy, Privacy, and Freedom; Student Presentations


Supporting reading material will be provided on a per-lecture basis.

Lecture notes and reading material can be found HERE.

Lern- und Lehrformen

  • Understand the data privacy concepts, and definitions
  • Learn to critically analyse data privacy algorithms and architectures in relation to data protection
  • Learn to identify the advantages and disadvantages of privacy preserving algorithms in relation to potential de-anonymisation loopholes
  • Aquire hands-on experience with re-identifying individuals from seemingly anonymous or innocent data
  • Learn to develop and assess privacy protocols, algorithms, and anonymity protection schemes to prevent inferences in shared data.


Grading will be based on an exam and a presentation. A selection pf presentation topics will be provided from which choices maybe made, but topic propositions are also acceptable provided these are discussed and agreed upon beforehand with the lecturer. The exam grade will count for 70% of the final score, while the presentation will count for 30%.

Rubric Number When? and Where? Grade %
Presentations 1 / person 07.01 - 04.02.2020 (H-2.58) 30%
Exam 1 11.02.2020 (H-2.58) 70%


Lectures will hold once a week, on Tuesdays, 13.30 - 15.00 in H-2.58.