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Pure differential privacy bounds relative entropy

Statement

Assume the Axiom of Choice. Let P and Q be probability measures on a measurable space measurably isomorphic to a Polish space, and let ε≥0. If

P(A)≤eεQ(A)andQ(A)≤eεP(A)

for every measurable set A, then their Kullback–Leibler divergence satisfies

D(P∥Q)≤ε(eε−1).

Facts & Assumptions

Given: AC, probability measures P,Q on a standard-Borel space, ε≥0, and both event inequalities in the Statement.

[A1]

Axiom of Choice is assumed by the Radon–Nikodym density and KL suppliers used below.

[F1]

Standard-Borel space is a measurable space of the stated standard-Borel class.

[F2]

Radon–Nikodym theorem: for finite probability measures with P≪Q, there is a measurable density r with P(A)=∫Ar dQ for every measurable A.

[F3]

Integration against a density: if r=dP/dQ, then integrating a nonnegative measurable function against P equals integrating its product with r against Q, whenever defined.

[F4]

KL divergence uses D(P∥Q)=∫log⁡(dP/dQ) dP when P≪Q, and assigns +∞ otherwise.

[F5]

KL nonnegativity gives D(R∥S)≥0 for probability measures under AC.

[F6]

Exponential monotonicity, exponential positivity and reciprocals, and the natural-logarithm reciprocal law imply a=eε≥1, a−1=e−ε>0, and log⁡(1/t)=−log⁡t for t>0.

[F7]

Borel composition preserves measurability; in particular the reciprocal map on (0,+∞) makes s=1/r measurable on the set where r>0.

Proof

1.1A1F1F2givenchoose

The standard-Borel hypotheses are explicit as required by [F1]. Since P(A)≤aQ(A) for every measurable A, P≪Q. Choose a Radon–Nikodym density r=dP/dQ using [A1] and [F2].

2.1F2F3F6step 1.1givenalgebra

By [F6], a=eε≥1 and a−1>0. The inequality P(A)≤aQ(A) forces r≤a Q-almost everywhere: otherwise some set En={r≥a+1/n} has positive Q-measure and violates the inequality after integration. Similarly Q(A)≤aP(A)=a∫Ar dQ forces r≥a−1 Q-almost everywhere, since any positive-measure set where r≤a−1−1/n violates that inequality. Thus a−1≤r≤a almost everywhere.

3.1F2step 2.1givenalgebra

Let E={r≥1}, p=P(E), q=Q(E), and t=p−q. Since P(A)−Q(A)=∫A(r−1) dQ, the set E maximizes this difference and Ec gives its negative. Hence t=sup⁡A∣P(A)−Q(A)∣=12∫∣r−1∣ dQ. The event inequalities on E and Ec give t≤(a−1)q and at≤(a−1)(1−q), respectively. Adding yields (a+1)t≤a−1, so t≤(a−1)/(a+1).

4.1A1F3F4F5F6F7step 2.1step 3.1algebra∎

The lower bound in step 2.1 makes P and Q equivalent. Define s=1/r on {r>0} and s=0 on its null complement; [F7] makes it measurable. By [F3], ∫As dP=∫Asr dQ=Q(A) for every measurable A, so s=dQ/dP. The logarithm reciprocal identity in [F6] gives log⁡s=−log⁡r almost everywhere, and both log densities are bounded in absolute value by ε; [F4] therefore gives finite divergences and D(P∥Q)+D(Q∥P)=∫(r−1)log⁡r dQ≤ε∫∣r−1∣ dQ=2εt. By [F5], D(Q∥P)≥0, and step 3.1 gives 2εt≤2ε(a−1)/(a+1)≤ε(a−1). Therefore D(P∥Q)≤ε(a−1)=ε(eε−1).

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