Data Privacy

Course Description

Data breaches, privacy breaches, and concerns about algorithmic decision-making have been on the rise. As a result, data privacy has become an increasingly significant concern in the past several years. Individuals and organizations often trust institutions with their data with the expectation that one's data is private from others or to the handling institutions and that it is not used for unfair practices. To ensure the privacy of sensitive data, privacy mechanisms have been developed to preserve the privacy of data without reducing its functionality. The goal of this course is to introduce you to the concept and implications of data privacy, including mechanisms and protocols that are used to preserve data privacy in practice. We will study concepts such as differential privacy, database anonymization, anonymous communication, and algorithmic fairness. We will also discuss privacy in the context of web privacy, social network privacy, human factors, and machine learning along with any policy implications. Prerequisites: COMP_SCI 211-0; COMP_SCI 214-0; COMP_SCI 262-0, GEN_ENG 231-0, ELEC_ENG 302-0, IEMS 201-0, IEMS 303-0, STAT 210-0 (COMP_SCI grad standing or consent of instructor).

Code

COMP_SCI 412-0

Credits

1

Subject

Computer Science

Programs That Include This Course

• Bachelor's Computer Science Degree
• Bachelor's Computer Science Major
• Minor Machine Learning and Data Science Minor