Dr. Anupam Das

Dr. Anupam Das

Computer Science

Phone: 919-515-2683
Instructor Website

CSC 533 Privacy in the Digital Age

3 Credit Hours

Privacy is a growing concern in our modern society. We interact and share our personal information with a wide variety of organizations, including financial and healthcare institutions, web service providers and social networks. Many times such personal information is inappropriately collected, used or shared, often without our awareness. This course introduces privacy in a broad sense, with the aim of providing students an overview of the challenging and emerging research topics in privacy.


Data Structures (NC State CSC 316)

Recommended: Probability and Statistics for Engineers (NC State ST 370)

Most assignments will require programming skills

Course Overview

This course will expose students to many of the issues that privacy engineers, program managers, researchers and designers deal with in industry. By the end of the course, students will learn about the following areas in privacy:
* Data privacy: the motivations for data privacy and common implementations (e.g., k-anonymity, differential privacy, information flow).
* Online privacy: online tracking and anonymous communication systems.
* Opportunities and implications of using AI/ML in privacy.
* Side-channel threats.
* Privacy acts: privacy regulations, frameworks and compliance/auditing tools.
* Usable privacy: perceptions of privacy, privacy attitudes, privacy preferences.

Course Outcomes

* Study different de-anonymization attacks on databases.
* Learn and apply techniques such as k-anonymity, l-diversity, t-closeness and differential privacy to anonymize and protect PII (personally identifiable information) in databases.
* Analyze and differentiate between various online tracking mechanisms and ways to mitigate such tracking techniques.
* Explain how anonymous communication networks like Tor work and how they help users preserve their online anonymity.
* Study privacy implications of using AI/ML on big data. Also explore ways to design better privacy-preserving and transparent ML algorithms.
* Understand various side-channel leaks and inference attacks.
* Analyze privacy regulations/frameworks (briefly look at existing and emerging privacy regulations).
* Compare and contrast users’ attitudes and perceptions of privacy under different contexts.
* Design and evaluate usable privacy notices.

Course Requirements

Homework 45%
Pop quizzes 5%
Midterm Exam 20%
Final Exam 30%


This course has no formal textbook. The course readings will come from online book chapters, seminal papers, and other informative sources. Slides will serve as the main reading resource summarizing the lecture content.
Here are some useful online books that provide additional information:
* Carmela Troncoso Privacy and Online Rights <>
* Lazar et al. Research Methods in Human-Computer Interaction <>
* Daniel Solove. Understanding Privacy <>
* Daniel J. Solove and Paul M. Schwartz. Consumer Privacy and Data Protection <>
* Helen Nissenbaum. Privacy in Context <>

Computer and Software Requirements

Please review minimum computer specifications recommended by NC State University and Engineering Online.

Updated 4/28/2020