Differential Privacy: A Comprehensive Survey from Theory to Practice
A recent survey paper published on arXiv (2509.03294v3) offers an in-depth exploration of differential privacy (DP), detailing its theoretical basis, practical implementations, and applications in various fields. The review responds to rising privacy issues linked to the growing accessibility of personal information, especially within machine learning, healthcare, and cybersecurity sectors. It examines essential algorithmic tools and specific challenges in different domains, with an emphasis on privacy-preserving machine learning and the generation of synthetic data. Additionally, the paper underscores the importance of usability and the necessity for better communication and transparency in DP systems, aiming to facilitate informed adoption of DP among researchers and practitioners.
Key facts
- arXiv paper ID: 2509.03294v3
- Announce type: replace-cross
- Covers theoretical foundations of differential privacy
- Discusses practical mechanisms and real-world applications
- Addresses privacy-preserving machine learning and synthetic data generation
- Highlights usability issues and need for transparency
- Target audience: researchers and practitioners
- Published on arXiv
Entities
Institutions
- arXiv