How statement and proof provenance work
The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.
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These labels describe origin, not correctness: citations and verification chips remain separate evidence.
Pure differential privacy bounds relative entropy
Statement
Assume the Axiom of Choice. Let and be probability measures on a measurable space measurably isomorphic to a Polish space, and let . If
for every measurable set , then their Kullback–Leibler divergence satisfies
Statement references
Facts & Assumptions
Given: AC, probability measures on a standard-Borel space, , and both event inequalities in the Statement.
Axiom of Choice is assumed by the Radon–Nikodym density and KL suppliers used below.
Standard-Borel space is a measurable space of the stated standard-Borel class.
Radon–Nikodym theorem: for finite probability measures with , there is a measurable density with for every measurable .
Integration against a density: if , then integrating a nonnegative measurable function against equals integrating its product with against , whenever defined.
KL divergence uses when , and assigns otherwise.
KL nonnegativity gives for probability measures under AC.
Exponential monotonicity, exponential positivity and reciprocals, and the natural-logarithm reciprocal law imply , , and for .
Borel composition preserves measurability; in particular the reciprocal map on makes measurable on the set where .
Proof
The standard-Borel hypotheses are explicit as required by [F1]. Since for every measurable , . Choose a Radon–Nikodym density using [A1] and [F2].
By [F6], and . The inequality forces -almost everywhere: otherwise some set has positive -measure and violates the inequality after integration. Similarly forces -almost everywhere, since any positive-measure set where violates that inequality. Thus almost everywhere.
Let , , , and . Since , the set maximizes this difference and gives its negative. Hence . The event inequalities on and give and , respectively. Adding yields , so .
The lower bound in step 2.1 makes and equivalent. Define on and on its null complement; [F7] makes it measurable. By [F3], for every measurable , so . The logarithm reciprocal identity in [F6] gives almost everywhere, and both log densities are bounded in absolute value by ; [F4] therefore gives finite divergences and . By [F5], , and step 3.1 gives . Therefore .
Depends on
- Differential privacy, databases, and add/remove adjacency
- Standard Borel spaces
- The Axiom of Choice
- Kullback–Leibler divergence
- A sigma-finite signed measure that is absolutely continuous with respect to a sigma-finite positive measure has a unique almost-everywhere density
- Integrating against a Radon-Nikodym derivative recovers integration against the measure
- Kullback–Leibler divergence is nonnegative
- The real exponential function and the number $e$ by a power series
- The exponential function is strictly increasing
- The exponential is positive and satisfies $\exp(-x)=1/\exp(x)$
- Order, continuity, range, and the product, quotient, and reciprocal laws for the natural logarithm
- Composition with a Borel measurable outer map preserves measurability
Used by
Dependency tree · two levels
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Sources
- Cynthia Dwork and Aaron Roth, The Algorithmic Foundations of Differential Privacy (standard reference, not scraped)
- Salil Vadhan, The Complexity of Differential Privacy, 2017 manuscript (standard reference, not scraped)