https://github.com/pallasite99/pac-gsu
Privacy Aware Computing
https://github.com/pallasite99/pac-gsu
blockchain georgia-state-university privacy privacy-enhancing-technologies web3
Last synced: over 1 year ago
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Privacy Aware Computing
- Host: GitHub
- URL: https://github.com/pallasite99/pac-gsu
- Owner: pallasite99
- Created: 2024-10-09T16:21:24.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-04T02:23:21.000Z (over 1 year ago)
- Last Synced: 2025-01-04T03:24:25.154Z (over 1 year ago)
- Topics: blockchain, georgia-state-university, privacy, privacy-enhancing-technologies, web3
- Language: Jupyter Notebook
- Homepage:
- Size: 1.51 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Privacy Aware Computing Coursework - Georgia State University
## Course Information
**Course Title**: Privacy Aware Computing
**Institution**: Georgia State University (GSU)
**Term**: [Fall 2024]
**Instructor**: [Zhipeng Cai]
---
## Overview
This repository documents the coursework completed for the **Privacy Aware Computing** course. The course covers theoretical and practical aspects of preserving privacy in computing systems, including privacy-enhancing technologies, legal frameworks, and ethical considerations.
---
## Learning Objectives
The key learning outcomes of the course include:
- Understanding foundational concepts of privacy in computing.
- Designing systems and algorithms that respect user privacy.
- Applying privacy-enhancing technologies (e.g., differential privacy, homomorphic encryption).
- Evaluating trade-offs between privacy, utility, and security.
- Familiarizing with laws and regulations (e.g., GDPR, CCPA) related to data privacy.
---
## Coursework Structure
### Assignments
- Analytical tasks to assess privacy risks in real-world systems.
- Practical implementation of privacy-preserving algorithms.
### Projects
- **Capstone Project**: Focused on designing a privacy-aware application or analyzing privacy risks in an existing system.
- Examples: Developing a differential privacy mechanism or implementing secure multi-party computation.
### Exams
- Midterm and final exams to test conceptual understanding and application of privacy-aware principles.
### Class Discussions
- Participation in discussions on contemporary topics such as AI ethics, surveillance, and the trade-offs between privacy and functionality.
---
## Tools and Technologies
This coursework utilized the following tools and technologies:
- **Programming Languages**: Python, Java
- **Libraries and Frameworks**: PySyft, TensorFlow Privacy
- **Cloud Platforms**: AWS for secure data storage and computation
- **Encryption Tools**: OpenSSL, PyCryptodome
---
## Key Deliverables
- Completed assignments and implementation scripts.
- Capstone project documentation and source code.
- Research paper or case study on privacy regulations.
- Exam preparation materials and class notes.
---
## Contact
For any questions about this coursework, feel free to reach out:
- **Name**: [Salil Dinesh Apte]
- **Email**: [salil.apte99@gmail.com]
- **LinkedIn**: [https://www.linkedin.com/in/salil-apte1112/]
---
## Acknowledgments
Special thanks to the instructor and classmates for fostering a collaborative and insightful learning environment.