SURV735: Data Privacy and Confidentiality
Teaching assistantship for SURV735 supporting graduate instruction in data privacy, confidentiality, and privacy-preserving statistical methods.
Graduate Course - Teaching Assistant, University of Maryland, Joint Program in Survey Methodology, 2025
Course Overview
Served as Teaching Assistant for SURV735: Data Privacy and Confidentiality under Dr. Jörg Drechsler from the Institute for Employment Research (IAB), Germany.
Responsibilities
- Guided 23 graduate students in understanding data privacy and confidentiality principles
- Assisted with course materials on statistical disclosure control and privacy-preserving methods
- Supported student projects on privacy-preserving data analysis techniques
- Facilitated discussions on differential privacy, synthetic data generation, and secure computation
- Graded assignments and provided detailed feedback on technical implementations
Topics Covered
| Topic | Description |
|---|---|
| Statistical Disclosure Control | Methods to protect confidential data in releases |
| Differential Privacy | Mathematical framework for privacy guarantees |
| Synthetic Data Generation | Creating privacy-preserving artificial datasets |
| Secure Multi-Party Computation | Collaborative computation without data sharing |
| Privacy Regulations | GDPR, CCPA, and federal statistical agency policies |
| De-identification Techniques | Anonymization methods and their limitations |
Teaching Impact
This teaching experience strengthened my understanding of:
- Privacy-preserving survey methodology and its practical applications
- Balancing data utility with confidentiality in research contexts
- Modern privacy technologies including differential privacy implementations
- Regulatory frameworks governing statistical data protection
Course Details
- Instructor: Dr. Jörg Drechsler (IAB, Germany)
- Program: Joint Program in Survey Methodology (JPSM)
- Level: Graduate (Masters/PhD)
- Term: Spring 2025