Privacy Preserving Algorithms and Data Security

Course
Course Catalog URL: 
http://www.registrar.iastate.edu/faculty-staff/courses/explistings/exp2017-2018a…
Identifier: 
COM S 4530
Professor(s): 
Meisam Mohammady

Last Updated: Fall 2024

  1. Credits and contact hours: 3 credits, 3 contact hours
  2. Instructor’s or course coordinator’s name: Meisam Mohammady
  3. Text book, title, author, and year: None required
  4. Other supplemental materials: None

Specific course information

  1. Brief description of the content of the course: New technologies have increasingly enabled corporations and governments to collect, analyze and share huge amount of data related to individuals. Today, the challenge is enabling the legitimate use of the collected data without violating privacy and security. In this course, we are going to analyze the fundamental models of ensuring data privacy and security, and explore potential theoretical models, algorithms, and technologies that can enhance data privacy and security in different systems and applications, such as recommenders, search engines, location-based services, social network, cloud computing, cryptocurrencies, and smart grid. We will also design and implement different techniques (e.g., cryptographic protocols, secure computation, and data sanitization) as well as examine their performance in terms of three critical properties: privacy/security, utility, and efficiency.
  2. Prerequisites or co-requisites: COM S 3110, STAT 3050 or STAT 3300 or STAT 3410 or STAT 3470
  3. Required, elective, or selected elective? Selected Elective

Specific goals for the course

  1. Specific outcomes of instruction:
  • Will learn the attacks to data security/privacy and different adversarial models.
  • Will provide the theoretical foundations of sanitizing different types of data while maximizing their utility for data analysis and AI applications.
  • Will provide the theoretical foundations of ensuring privacy and security for data ubiquitously collected from different sites, including applied cryptography, secure multiparty computation, and secure communication protocols.
  • Will learn the design and implementation of privacy preserving data sanitization algorithms for large-scale datasets.
  • Will learn the design and implementation of cryptographic protocols for data analysis and AI applications in distributed computing environment.

Brief list of topics to be covered

  • De-anonymization Attacks
  • Data Anonymization
  • Differential Privacy
  • Data Aggregation
  • Data Mining with DP
  • Local Differential Privacy and Deep Learning
  • Basic Cryptography
  • Secure Multiparty Computation
  • Garbled Circuit
  • Secret Sharing
  • Data Integrity