70018
Privacy Engineering
Module aims
This module will teach you about state-of-the-art privacy-preserving mechanisms and systems and how to attack them. We will then review and discuss improvements and trade-offs.
First, we will review anonymisation strategies for small and big data datasets and their limits. We will then do the same for query-based systems before learning and applying a theory called Differential Privacy. Finally, throughout the module, we will read and discuss research papers on attack and defenses.
Learning outcomes
Upon successful completion of this module you will be able to:
- anonymise and re-identify small and big data datasets
- use and extend algorithms to attack query-based/ question-and-answers systems
- use and develop differentially-private functions and evaluate the use of differential privacy on personal data sets
Module syllabus
This module covers the following topics:
- Pseudonymisation and anonymisation
- Big data anonymisation
- Query-based systems
- Differential privacy
Teaching methods
The module is motivated by real-world privacy concerns and aims to introduce the fundamental concepts and techniques for developing solutions that are privacy-preserving. In addition to material prepared by the lecturers you will also be expected to read and discuss selected research papers in privacy and security. The objective is to prepare you to pursue research into privacy as well as to develop and adversarially test privacy-preserving solutions to industry problems.
We have a mix of lectures, labs and student presentations. Labs are not assessed.
An online service will be used as an open discussion forum for the module.
Assessments
Coursework will consist of the following: a group presentation and discussion about a research paper. The coursework counts for 20% of the marks for the module. There will be a final written exam, which will test both theoretical and practical aspects of the subject. This exam counts for the remaining 80% of the marks.
Some feedback will be provided on the coursework which will include oral feedback and class-wide feedback explaining common pitfalls and suggestions for improvement.