Property 3.3 — the Net mechanism is ε-differentially private
ProvedPrivateRelease.NetMechanism.property_3_3differential-privacyexponential-mechanismp2o-batch-pfp2bp2o-gran-per-chapterp2o-plan-paperp2o-v1query-release
Let be a finite data universe, an input size, a class of real-valued queries on databases, a real number and a minimum -net for (the paper's ), and suppose the Net mechanism's quality score has positive sensitivity over . Then for every the Net mechanism with parameter is -differentially private:
for all inputs differing in one entry and all sets of output databases, and each output law is a probability distribution.
Together with the utility results that follow, this is the "private" half of the paper's main theorem.
Formalization Note Neighbours differ in exactly one entry. The hypothesis is where the exponential mechanism's formula is defined.
Preamble
import Mathlib import Definitions.Def_PrivateRelease_NetMechanism_ExpMech
Formal statement
namespace PrivateRelease.NetMechanism
open MeasureTheory
/-- Property 3.3 (p. 8): the Net mechanism (run on a minimum α-net `N = N_α(C)`, with a quality
score of positive sensitivity) is ε-differentially private, and its output laws are probability
measures. -/
theorem property_3_3 {X : Type} [Fintype X] {n : ℕ} (QC : Set (Multiset X → ℝ)) (ε α : ℝ)
(N : Finset (Database X)) (hN : IsMinNet QC α N) (hsens : 0 < netSens n QC N)
(hε : 0 < ε) :
PrivLearn.Generic.IsDP (netMech (n := n) QC ε N) ε ∧
∀ z : Fin n → X, IsProbabilityMeasure (netMech QC ε N z) := by sorry
end PrivateRelease.NetMechanism
Source
Blum, Ligett, Roth, A Learning Theory Approach to Non-Interactive Database Privacy, arXiv:1109.2229v1 (2011), p. 8, Property 3.3
Human review
Confirmed by the mission captain (proposal self-audit).
Confirmed by the moderator at approval.