CS 410/510: Introduction to Privacy-aware Computing

Course Description

Organizations today collect, store, analyze, and process user data for various purposes. Often, this data is managed without considering user privacy which puts user’s sensitive information at risk. In this course, we will study privacy in a broad sense and look at the challenges and opportunities of incorporating privacy into computing.

In the course, we will explore the following topics:

• What and Why of Privacy?
• Principles of Privacy-aware computing/Privacy-by Design
• Privacy Regulations and compliance
• Privacy Policies
• Access control and Inference control
• Deidentification and Generalization techniques
• Differential Privacy
• Geo-Indistinguishability
• Privacy-preserving Machine Learning
• Federated Learning

… and more topics depending upon time.

Please see the schedule for final lecture topics and dates. In addition to the instructor lectures, the course will feature guest speakers presenting their latest research on topics on some of these topics.

Learning Outcomes

Upon completion of this course, students will be able to

• Explain the importance of the privacy in computing
• Describe various kinds of challenges to protecting privacy of individuals data
• Learn about different approaches to privacy-aware computing, encompassing the what, how, and when of using these techniques.
• Reason about trade-offs between privacy and other goals (e.g., utility, usability) of computing
• Apply principles of Privacy-by Design/Privacy Engineering
• Explore privacy regulations and how to check for compliance
• Develop intuitions for privacy and think like a privacy engineer/researcher

Prerequisites

• Prior course work in algorithms, databases, and probability will be helpful for certain topics covered in the class.
• Comfortable with programming for the course project and in-class activities.
• Curiosity for research.

If you are unsure about any of the requirements, get in touch with the instructor.

Textbook

This course does not require a formal textbook. Instead, the course readings will be derived from online articles, seminal research papers, and other relevant sources. The syllabus and slides will offer both required and supplementary reading resources for this class.

Grading Criteria

• Paper reading and discussion: 20% (Refer to the paper reading page)
• Project: 60% (Refer to the project page)
• In-class and online Quizzes: 12% (4 x 3%); best 3 of 4.
• Class participation: In-person and online activities: 8%
• Data Privacy day task: Extra credit

No other homeworks, take home assignments, mid-terms, or finals.

Communication

• Canvas Discussions: Offical class communication channel for finding teammates, paper discussions, and asking questions.
• Slack: Join the #intro-privacy-winter24 channel on PSU slack
• Office hours: In-person or via Zoom.
• Email: Use either [CS410] or [CS510] in the subject of the email. Expected response time for emails is 48 hours. Feel free to send follow-up reminders if you haven’t received replies beyond this time. Failure to follow this instructions will result in delay of response.

Course Policies

Please refer to the policies page.

Flexibility Statement

The instructor reserves the right to modify course content and/or substitute assignments and learning activities in response to institutional, weather, or class situations.

Take care of yourself!

As a student, you may experience a wide variety of challenges to your physical and mental health, that can interfere with learning. Help is available on campus and an important aspect of taking care of yourself is learning how to ask for help. Talk to the instructor, if you are struggling with any aspect of the course. Ask for help early. We cannot change the past, but can influence the future. Confidential counseling services are available at PSU. Please refer to the Student Crisis Resource Card for a list of phone numbers, contacts and support resources.