Simon Fraser University
Engaging the World

Course Outlines

Fall 2026 - CMPT 479 E100

Special Topics in Computing Systems (3)

Privacy Preserving Graph Analytics

Class Number: 6549

Delivery Method: In Person

Overview

  • Course Times + Location:

    Sep 9 – Dec 6, 2026: Tue, 4:30–7:20 p.m.
    Burnaby

  • Prerequisites:

    CMPT 201 or CMPT 300 or ENSC 351, with a minimum grade of C-.

Description

CALENDAR DESCRIPTION:

Current topics in computing systems depending on faculty and student interest.

COURSE DETAILS:

Title: AI in Security
Prerequisites: CMPT 225 and CMPT 307 and STAT 271, each course with a minimum grade of C+

Privacy-preserving graph analytics seeks to extract valuable insights from networked data while protecting sensitive information about individuals and their relationships. This challenge is increasingly important as modern graphs often represent social interactions, communication patterns, healthcare records, financial transactions, and other forms of interconnected data. Even when identifiers are removed, graph structures can reveal private information through re-identification attacks and inference of sensitive attributes. Addressing these risks requires rigorous privacy-preserving techniques that enable data analysis without exposing individual entities, connections, or behaviors.

A central focus of this course is differential privacy, a mathematically rigorous framework that limits the information that can be learned about any individual from the results of an analysis. In graph settings, differential privacy can protect sensitive relationships, community memberships, and activity patterns while still supporting useful graph mining tasks. The course also explores complementary privacy-enhancing technologies, including secure multi-party computation, homomorphic encryption, and cryptographic techniques that enable privacy-preserving computation and support differentially private data analysis. Together, these approaches provide a foundation for designing, analyzing, and deploying trustworthy graph analytics systems on sensitive 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.

Department Undergraduate 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 D 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).

 

  • Use of AI tools by students for completing graded scholarly activities and course submissions that has not been explicitly authorized by the instructor  will be considered as plagiarism under SFU’s Code of Academic Policy (http://www.sfu.ca/policies/teaching/t10-02.htm).  Following this policy, instructors will report all instances of plagiarism to the academic integrity board.  

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.