Differential Privacy Attacks

Differential privacy (DP) adds calibrated noise to model training or outputs to bound the information leakage about any individual training record. Attacks against DP exploit the gap between the formal mathematical guarantee and its practical implementation. The core vulnerability is that DP's privacy budget (ε) is finite and compositional — every query consumes part of the budget, and once exhausted, subsequent queries receive no privacy protection.

• Anomalous query volume: repeated similar queries from the same source signal noise-averaging attacks
• Temporal query pattern analysis: queries clustered around budget refresh windows indicate timing exploitation
• Cross-query correlation detection: queries from the same source spanning multiple query types targeting overlapping data
• API usage analytics: flag accounts with statistically unusual query distributions

Successful DP bypass enables T10-AT-003 (Membership Inference) against DP-protected models, and provides clean data for T10-AT-001 (Training Data Extraction) by removing the noise floor that otherwise prevents extraction.