Extractability
InductiveMerkleTree.extractability
Project documentation
The extractability theorem for Merkle trees. Adapting from the SNARGs book Lemma 18.5.1: For any adversary ๐ whose committing and opening phases together obey the two-phase total query bound qb, if the game runs ๐.commit and ๐.opening, and the extractor algorithm is run on the resulting cache and root, then with probability at most ฮบ does `๐...
Exact Lean statement
theorem extractability [DecidableEq ฮฑ] [Fintype ฮฑ] [Inhabited ฮฑ]
{s : Skeleton} (๐ : Adversary ฮฑ s) (qb : โ)
(h_IsQueryBound_qb : ๐.IsTwoPhaseTotalQueryBound qb) :
Pr[AdversaryWinsExtractabilityGame |
extractabilityGame ๐] โค
((qb + s.depth) ^ 2 : ENNReal) / (2 * Fintype.card ฮฑ)
+ 1 / (Fintype.card ฮฑ)Formal artifact
Lean source
theorem extractability [DecidableEq ฮฑ] [Fintype ฮฑ] [Inhabited ฮฑ] {s : Skeleton} (๐ : Adversary ฮฑ s) (qb : โ) (h_IsQueryBound_qb : ๐.IsTwoPhaseTotalQueryBound qb) : Pr[AdversaryWinsExtractabilityGame | extractabilityGame ๐] โค ((qb + s.depth) ^ 2 : ENNReal) / (2 * Fintype.card ฮฑ) + 1 / (Fintype.card ฮฑ) := by calc _ = Pr[AdversaryWinsExtractabilityGame โ Prod.fst | (extractabilityGame ๐).withQueryLog] := (probEvent_withQueryLog _ _).symm _ โค Pr[fun (vals, log) => LogHasCollision log โจ (ยฌ LogHasCollision log โง AdversaryWinsExtractabilityGame vals) | (extractabilityGame ๐).withQueryLog] := probEvent_mono'' fun โจ_, _โฉ => by tauto _ โค Pr[fun (vals, log) => LogHasCollision log | (extractabilityGame ๐).withQueryLog] + Pr[fun (vals, log) => ยฌ LogHasCollision log โง AdversaryWinsExtractabilityGame vals | (extractabilityGame ๐).withQueryLog] := probEvent_or_le .. _ โค ((qb + s.depth) ^ 2 : ENNReal) / (2 * Fintype.card ฮฑ) + Pr[fun (vals, log) => ยฌ LogHasCollision log โง AdversaryWinsExtractabilityGame vals | (extractabilityGame ๐).withQueryLog] := by gcongr convert OracleComp.probEvent_logCollision_le_birthday_total (spec := spec ฮฑ) (extractabilityGame ๐) (qb + s.depth) (extractabilityGame_isTotalQueryBound ๐ qb h_IsQueryBound_qb) (fun _ => le_rfl) using 2 ยท rfl all_goals norm_cast _ โค ((qb + s.depth) ^ 2 : ENNReal) / (2 * Fintype.card ฮฑ) + 1 / (Fintype.card ฮฑ) := by have h' := extractabilityGame_not_logHasCollision_wins_le_inv_card ๐ gcongr; norm_cast- Project
- VCVio
- License
- Apache-2.0
- Commit
- 2ceb2d825ee3
- Source
- VCVio/CryptoFoundations/MerkleTree/Inductive/Extractability.lean:678-714
Reuse this declaration
Bring the exact result into your workflow
The import identifies the source module. Your project still needs the pinned package dependency shown on this page.
What this badge means
This completion status comes from the project or community source. It has not yet been represented here as an independent rebuild and axiom audit.
Continue in this project
Related declarations
Expected Cost Nat eq sum tail probs of pathwise Cost At Most
AddWriterT.expectedCostNat_eq_sum_tail_probs_of_pathwiseCostAtMost
Plain-language statement
Finite tail-sum formula for natural-valued writer cost under a pathwise upper bound. If every execution path of oa incurs cost at most n, then the tail probabilities vanish above n, so the infinite tail sum truncates to Finset.range n.
Source project: VCVio
Person-level attribution pending.
IND CPA advantage to Real le sum step signed Advantage Real abs
AsymmEncAlg.IND_CPA_advantage_toReal_le_sum_step_signedAdvantageReal_abs
Plain-language statement
Planned generic one-time-to-many-time lift: bounded multi-query IND-CPA advantage is at most the sum of the extracted one-time signed advantages over the first q fresh LR queries.
Source project: VCVio
Person-level attribution pending.
IND CPA LR hybrid Game q eval Dist eq left of Makes At Most Queries
AsymmEncAlg.IND_CPA_LR_hybridGame_q_evalDist_eq_left_of_MakesAtMostQueries
Plain-language statement
If an adversary makes at most q fresh LR queries, then the leftUntil = q LR-hybrid is the all-left endpoint game.
Source project: VCVio
Person-level attribution pending.