Cybersecurity 233
Privacy Engineering
3 units
Course Description
This course surveys privacy mechanisms applicable to systems engineering, with a particular focus on the inference threat arising due to advancements in artificial intelligence and machine learning. We will briefly discuss the history of privacy and compare two major examples of general legal frameworks for privacy from the United States and the European Union. We then survey three design frameworks of privacy that may be used to guide the design of privacy-aware information systems. Finally, we survey threat-specific technical privacy frameworks and discuss their applicability in different settings, including statistical privacy with randomized responses, anonymization techniques, semantic privacy models, and technical privacy mechanisms.
Student Learning Outcomes
- Students should be familiar with the different technical paradigms of privacy that are applicable for systems engineering.
- Students should develop critical thinking about the strengths and weaknesses of the different privacy paradigms.
- Students should be able to implement such privacy paradigms, and embed them in information systems during the design process and the implementation phase.
- Students should possess the ability to read literature in the field to stay updated about the state of the art.
Previously listed as CYBER W233.
Prerequisites
Video
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Course History
Fall 2023
Spring 2023
Fall 2022
Summer 2022
Spring 2022
Fall 2021
Summer 2021
Spring 2021
Fall 2020
Summer 2020
Spring 2020
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