Prerequisites: Security and Cryptography (Crp). Click the checklist “Do I meet the prerequisites?”
Motivation: Concepts like the Internet of Things or Big Data inherently utilize massive amounts of data containing private information collected and stored by websites, sensors, monitoring systems, auditing systems, and so on. Examples include electronic records in health care systems and location information in ubiquitous computing applications. But how can we protect the privacy of participating users while at the same time enable effective sharing and utilization of the distributed data?
Synopsis: There are several dimensions in the area of privacy, ranging from technical and juridical to societal and economical. While we will touch upon all these different aspects in the course, we will focus on the technical dimension. We will explore potential techniques for building new platforms, services, and tools that protect users' privacy. The study of promising component technologies ranging from advances in anonymous communication and identity management to tools like differential privacy and cryptography will be the core of this course.
Learning outcomes: The student will obtain:
- Good overview of privacy aspects from both a societal and business perspective
- Ability to analyze and evaluate anonymity mechanisms, both for anonymous communication and for database privacy
- Ability to apply and analyze the concept of secure multiparty computation to protect privacy in different application domains
- Gain hands-on experience with different privacy-enhancing technologies
Lecturers: Dr Zeki Erkin (TUD), Dr Kaitai Liang (TUD), Dr Stefanie Roos (TUD)
Examination: Written exam (60%-closed book); (practical) assignments (40%)
Contents: Overview on societal, juridical and economical aspects of privacy; anonymous communication mechanisms; mix networks; onion routing; TOR; privacy in identity management; anonymous credentials; zero-knowledge proofs of knowledge; commitment schemes; oblivious transfer; privacy in electronic voting; database privacy; k-anonymity; differential privacy; other probabilistic approaches for database privacy; private data processing; secure multiparty computation; (fully/somewhat) homomorphic encryption; garbled circuits; secret sharing; privacy-preserving clustering; private recommender systems; private smart metering
Core text: Various papers from the literature