Simon Fraser University
Engaging the World

Course Outlines

Fall 2025 - CMPT 982 G300

Special Topics in Networks and Systems (3)

Class Number: 7454

Delivery Method: In Person

Overview

  • Course Times + Location:

    Sep 3 – Dec 2, 2025: Thu, 4:30–7:20 p.m.
    Burnaby

  • Exam Times + Location:

    Dec 11, 2025
    Thu, 7:00–10:00 p.m.
    Burnaby

Description

COURSE DETAILS:

Recent advances in graph mining enable the discovery of complex relationships in large, real-world networks. As these graphs often contain sensitive entities and connections, protecting privacy is essential. For example, social networks reveal friendships, interactions, and group affiliations, while healthcare graphs may link patients, treatments, and diagnoses. These expose highly personal information. Without strong privacy guarantees, releasing such data can lead to re-identification or inference of private details, even from seemingly anonymized graphs.

Differential privacy is a mathematical framework that ensures an individual's data does not significantly affect the outcome of any analysis, providing strong privacy guarantees regardless of what external information is known. For instance, differential privacy can protect users' friend connections, group affiliations and activity patterns when releasing social network insights, ensuring no one can tell if a specific person was in the data.

This course explores recent advances in privacy-preserving graph analytics, with a focus on differential privacy. We will explore key concepts such as the fundamental meaning of privacy for graph data, along with the challenges and solutions in areas like differentially private graph publishing, private graph querying (e.g., paths, degree distribution), and private subgraph and pattern mining.

COURSE-LEVEL EDUCATIONAL GOALS:

This seminar course involves reading, presenting, and discussing recent research papers on privacy-preserving graph analytics. Students will gain a deep understanding of the field through critical analysis and discussions. In addition, students will work on a term project, applying concepts to explore a research question or develop a prototype solution.

Grading

NOTES:

To be discussed in the first class.

REQUIREMENTS:

Students must have an understanding of basic probability and statistics, foundations of algorithms and data structures, basic graph theory, and basic programming (e.g., Python, SQL). Familiarity with linear algebra or data privacy as well as tools like Pandas, NetworkX, or graph databases is also useful.

Materials

MATERIALS + SUPPLIES:

Course materials primarily consist of research papers from top conferences and journals. Reading list to be discussed in the first class.

The following books may be used to refresh background and build foundational knowledge.
* The Algorithmic Foundations of Differential Privacy. Cynthia Dwork and Aaron Roth.
* Programming Differential Privacy. Joseph P. Near and Chiké Abuah.
* Network Science. Albert-László Barabási.

REQUIRED READING NOTES:

Your personalized Course Material list, including digital and physical textbooks, are available through the SFU Bookstore website by simply entering your Computing ID at: shop.sfu.ca/course-materials/my-personalized-course-materials.

Department Graduate Notes:

The following are default policies in the School of Computing Science. Please check your course syllabus whether the instructor has chosen a different policy for your class, otherwise the following policies apply.
 
  • Students must attain an overall passing grade on the weighted average of exams in the course in order to get a C- or higher.
  • All student requests for accommodations for their religious practices must be made in writing by the end of the first week of classes, or no later than one week after a student adds a course. After considering a request, an instructor may provide a concession or may decline to do so. Students requiring accommodations as a result of a disability can contact the Centre for Accessible Learning (caladmin@sfu.ca).

Graduate Studies Notes:

Important dates and deadlines for graduate students are found here: http://www.sfu.ca/dean-gradstudies/current/important_dates/guidelines.html. The deadline to drop a course with a 100% refund is the end of week 2. The deadline to drop with no notation on your transcript is the end of week 3.

Registrar Notes:

ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS

At SFU, you are expected to act honestly and responsibly in all your academic work. Cheating, plagiarism, or any other form of academic dishonesty harms your own learning, undermines the efforts of your classmates who pursue their studies honestly, and goes against the core values of the university.

To learn more about the academic disciplinary process and relevant academic supports, visit: 


RELIGIOUS ACCOMMODATION

Students with a faith background who may need accommodations during the term are encouraged to assess their needs as soon as possible and review the Multifaith religious accommodations website. The page outlines ways they begin working toward an accommodation and ensure solutions can be reached in a timely fashion.