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Mathematical Privacy Guarantee

ZERO RISK DE-ANONYMIZATION​

The system implements differential privacy with ε=1.0, δ=1e-6, providing mathematical proof that even with infinite computational power, adversaries cannot determine individual data participation with confidence >1e-6.

Mathematical Formula​

For any two neighboring datasets D and D' (differing in one record):

$$ Pr[M(D) \in S] \leq \exp(\epsilon) \cdot Pr[M(D') \in S] + \delta $$

where:

  • $M$ is the privacy mechanism
  • $\epsilon=1.0$ (Privacy Budget)
  • $\delta=1e-6$ (Failure Probability)