DAS 715 – Privacy-Preserving Data Analysis

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About Course

This course explores methods for analyzing data while preserving privacy, focusing on techniques such as differential privacy, secure multi-party computation, and homomorphic encryption. Students will examine case studies and apply these methods to real-world scenarios, learning how to balance the need for data utility with the requirement to protect individual privacy. The course prepares students to implement privacy-preserving data analysis techniques in various domains, including healthcare, finance, and social networks.

What Will You Learn?

  • Understand the principles and importance of privacy-preserving data analysis.
  • Apply differential privacy techniques to protect sensitive information.
  • Implement secure multi-party computation for collaborative analytics.
  • Utilize homomorphic encryption for computation on encrypted data.
  • Analyze privacy-utility trade-offs in data-driven decision making.
  • Evaluate ethical and legal considerations in privacy-preserving systems.
  • Explore privacy-preserving applications in healthcare, finance, and social networks.
  • Use modern tools and frameworks for secure data analysis.

Course Content

Week 1: Introduction to Privacy-Preserving Data Analysis

  • Week 1: Introduction to Privacy-Preserving Data Analysis
    00:00
  • LO1: Define the key concepts and importance of privacy-preserving data analysis in data science.
    00:00
  • LO2: Explain different privacy-preserving techniques used to protect sensitive information.
    00:00
  • LO3: Describe the role of privacy-preserving methods in healthcare, finance, and social networks.
    00:00
  • Multiple Choice Questions
  • True / False
  • Scenario-Based Multiple Choice Questions
  • Short Answer Questions
  • Key Terms
  • Written Assignment
  • Presentation Task
  • Role-Playing Activity
  • Peer Review Task
  • Exercises and Activities Adaptation

Week 2: Differential Privacy I

Week 3: Differential Privacy II

Week 4: Secure Multi-Party Computation I

Week 5: Secure Multi-Party Computation II

Week 6: Homomorphic Encryption I

Week 7: Homomorphic Encryption II

Week 8: Midterm Test or Assignment

Week 9: Case Studies in Privacy-Preserving Data Analysis

Week 10: Trade-offs Between Privacy and Data Utility

Week 11: Ethical and Legal Considerations

Week 12: Tools and Frameworks for Privacy-Preserving Data Analysis

Week 13: Emerging Trends in Privacy-Preserving Data Analysis

Week 14: Future Challenges and Opportunities

Week 15: Course Review

Week 16: Final Test or Project

Florida Coastal University

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