> ML_LITERATURE // DWORK-2006-DIFFERENTIAL-PRIVACY_v1.0
Differential Privacy
Cynthia Dwork · International Colloquium on Automata, Languages, and Programming (ICALP) (2006)
foundational2006industry-standardthirdPartyReproduced
Principal Contribution
Mathematically defined (epsilon, delta)-differential privacy, guaranteeing that an individual presence cannot be inferred from query results.
Operational Relevance
Directly guides deployment choices and architecture selection for task-privacy-preserving-ml.
Assumptions
- Standard empirical regularity and statistical stability hold across evaluation domains
Limitations
- Performance characteristics depend on domain distribution and compute allocation parameters
Connected Algorithms, Architectures & Tools
Related Algorithms:
Related Architectures:
Implementing Libraries: