logos-storage-docs-obsidian/10 Notes/Component Specification - Prover.md

Proving Module Specification

1. Purpose and Scope

Purpose/Motivation

In decentralized storage networks, such as Codex one of the main challenges is ensuring durability and availablity of the data stored by storage providers (SP). To achieve durability, random sampling (along with erasure coding) is used to provide probabilistic guarantees while touching only a tiny fraction of the stored data per challenge.

The primary purpose of the proving module is to provide a succinct, publicly verifiable way to check that an SP still holds the data it committed to. It samples cells from the stored slot, and turns those samples and Merkle paths into a small Groth16 proof that ties back to the published dataset root. The marketplace contract verifies this proof on-chain and uses the result to manage incentives e.g. payments and slashing.

Scope

The scope of this specification document is limited to the proving module which consists of the following functionalities:

• Checks for storage proof requests.
• Samples cells from a slot in the stored dataset and constructs Merkle proofs.
• Generates zero-knowledge proofs for randomly selected cells in the stored slots.
• Submits proofs to verifier in the network (the on-chain marketplace smart contract).

The Proving module relies on blockStore for storage and SlotsBuilder to build initial commitments to the stored data. Additionally, this document doesn't specify the incentives involved (collateral and slashing), which is handled by the marketplace logic.

Terminology

Term	Description
Storage Client (SC)	A node that participates in Codex to buy storage.
Storage Provider (SP)	A node that participates in Codex by selling disk space to other nodes.
Dataset	A set of fixed-size slots provided by (possibly different) SCs.
Cell	Smallest circuit sampling unit (e.g., 2 KiB), its bytes are packed into field elements and hashed.
Block	Network transfer unit (e.g., 64 KiB) consists of multiple cells, used for transport.
Slot	The erasure-coded fragment of a dataset stored by a single SP. proof requests are related to slots.
Commitment	Cryptographic binding (and hiding if needed) to specific data. In Codex this is a Poseidon2 Merkle root (e.g., Dataset Root or Slot Root). It allows anyone to verify proofs against the committed content.
Dataset Root / Slot Root	Poseidon2 Merkle roots used as public commitments in the circuit. Not the SHA-256 content tree used for CIDs.
Entropy	Public randomness (e.g., blockhash) used to derive random sample indices.
Witness	Private zk circuit inputs.
Public Inputs	Values known to (or shared with) the verifier.
Groth16	Succinct zk-SNARK proof system used by Codex.
Proof Window	The time/deadline within which the SP must submit a valid proof.

2. Interfaces

Component Overview

The Prover module consists of three main components:

1. Builder: turns raw bytes into zk-SNARK-friendly commitments.
2. Sampler: derives random sample indices from public entropy and slot commitments, and then generates the proof input.
3. Prover: produces succinct ZK proofs for valid proof inputs, and verifies such proofs.

Component Interfaces

Below are the main interfaces for each component. The interfaces below omit getters, setters, and internal functions, instead it focuses on main functions needed. T denotes the Merkle tree type (supports all Merkle tree functionality). H denotes the digest/hash element type (e.g., Poseidon2Hash) used for leaves, paths, and roots.

Builder interfaces:

Interface	Description	Input	Output
new	Create a new SlotsBuilder instance.	blockStore: BlockStore, manifest: Manifest, strategy: IndexingStrategyType, cellSize: NBytes	SlotsBuilder[T,H]
buildBlockTree	Build the block Merkle tree and return the block data and the tree.	blkIdx: Natural, slotPos: Natural	(seq[byte], T)
buildSlotTree	Build the slot Merkle tree from the block-tree roots and return the tree. Calls buildBlockTree internally.	slotIndex: Natural	T
buildVerifyTree	Build the verification (dataset) Merkle tree from slot roots.	slotRoots: openArray[H]	T
buildSlots	Build all slot trees (with buildSlotTree ) in the dataset and store their proofs in the BlockStore. Builds the dataset verification tree via buildVerifyTree.	—	void

Sampler interfaces:

Interface	Description	Input	Output
new	Construct a DataSampler for a specific slot index.	index: Natural, blockStore: BlockStore, builder: SlotsBuilder[T,H]	DataSampler[T,H]
getSample	Retrieve one sampled cell and its Merkle path(s) for a given slot.	cellIdx: int, slotTreeCid: Cid, slotRoot: H	Sample[H]
getProofInput	Generate the full proof inputs for the proving circuit (calls getSample internally).	entropy: ProofChallenge, nSamples: Natural	ProofInputs[H]

Prover interfaces:

Interface	Description	Input	Output
new	Construct a Prover with a block store and the backend proof system.	blockStore: BlockStore, backend: AnyBackend, nSamples: int	Prover
prove	Produce a succinct proof for the given slot and entropy.	slotIdx: int, manifest: Manifest, entropy: ProofChallenge	(proofInputs, proof)
verify	Verify a proof against its public inputs.	proof: AnyProof, proofInputs: AnyProofInputs	bool

3. Functional Requirements

Data Commitment

• Build slot commitments: Construct cell -> block -> slot Merkle trees for each slot from locally stored bytes using Poseidon2 (cell hashing) and keyed compression.
• Build dataset commitment: Construct slot -> dataset verification tree.
• Proof material: retrieve cell data (and optionally proofs) from the BlockStore.

Sampling

• checks for marketplace challenges per slot.
• Deterministic sampling: Derive nSamples cell indices for the slotIndex from (entropy, slotRoot).
• For each sampled cell, obtain/fetch: cellData (as field elements) and Merkle paths (all cell -> block -> slot -> dataset)
• Generate ProofInputs containing the public inputs (datasetRoot, slotIndex, entropy) and private witness (cellData, slotRoot, MerklePaths).

Proof Generation

• Given ProofInputs, use the configured backend (Groth16 over BN254) to create a succinct Groth16 proof.
• The circuit enforces the same Merkle layout and Poseidon2 hashing used for commitments

4. Non-Functional Requirements

Performance / Latency

• End-to-end (sample -> prove -> submit) completes within the on-chain proof window with some safety margin.
• Small Proof size, e.g. Groth16/BN254 plus public inputs.
• On-chain verification cost is minimal.
• Support concurrent proving for multiple slots.

Security & Correctness

• Soundness and completeness: only accept valid proofs, invalid inputs must not yield accepted proofs.
• Commitment integrity: proofs are checked against the publicly available Merkle commitments to the stored data.
• Entropy binding: sample indices must be derived from the on-chain entropy and the slot commitment.

5. Internal Behavior

High-level Overview

At a high level, the proving module contains three components each with specific roles:

• Builder: Constructs the dataset’s Merkle tree commitment from the stored dataset (fetched using BlockStore and the Manifest). It generates the dataset-level verificationTree (datasetRoot and slot -> dataset paths) and, for each block a Merkle tree with cell -> block paths.
• Sampler takes a challenge (slotIndex, entropy, nSamples) and maps it to cell indices within that slot. For every sampled cell, it fetches the cellData and concatenates the necessary Merkle paths: cell -> block, block -> slot, and slot -> dataset (from the BlockStore).
• Prover takes ProofInputs and asks the proving system backend (Groth16) to produce a succinct proof. Verification can be done on-chain or off-chain against the same inputs.

The flow consists of the following steps:

1. Data Commitment
2. Sampling
3. Generating a ZK Proof

Flow

1. Data Commitment In Codex, a dataset is split into numSlots slots which are the ones sampled. Each slot is split into nCells fixed-size cells. Since networking operates on blocks, cells are combined to form blocks where each block contains BLOCKSIZE/CELLSIZE cells. The following descibes how raw bytes become commitments in Codex (cells -> blocks -> slots -> dataset):

!

• Cell hashing:

  • Split each cell’s bytes into chunks (31-byte for BN254), map to field elements (little-endian). Pad the last chunk with 10*.
  • Hash the resulting field-element stream with a Poseidon2 sponge.

• Block tree (cell -> block):

  • A network block in Codex is 64kb and contains 32 cells of 2kb each.
  • Build a Merkle tree of depth 5 over the 32 cell hashes. The root is the block hash.

• Slot tree (block -> slot):

  • For all blocks in a slot, build a Merkle tree over their block hashes (root of block trees). The number of leaves is expected to be a power of two. The Slot tree root is the public commitment that is sampled.

• Dataset tree (slot -> dataset):

  • Build a Merkle tree over the slot trees roots to obtain the dataset root (this is different from SHA-256 CID used for content addressing). The dataset root is the public commitment to all slots hosted by a single SP.

Codex Merkle tree conventions Codex extends the standard Merkle tree with a keyed compression that depends on (a) whether a node is on the bottom (i.e. leaf layer) and (b) whether a node is odd (has a single child) or even (two children). These two bits are encoded as {0,1,2,3} and fed into the hash so tree shape can’t be manipulated.

Steps in building the Merkle tree (bytes/leaves -> root)

Cell bytes are split into 31-byte chunks to fit in BN254, each mapped little-endian into a BN254 field element. 10* padding is used.

Leaves (cells) are hashed with a Poseidon2 sponge with state size t=3 and rate r=2. The sponge is initialized with IV (0,0, domSep) where
domSep := 2^{64} + 256*t + rate (a domain-separation constant).

When combining two child nodes x and y, Codex uses the keyed compression:

compress(x, y, key) = Poseidon2_permutation(x, y, key)[0]

where key encodes the two bits:

• bit 0: 1 if we’re at the bottom layer, else 0
• bit 1: 1 if this is an odd node (only one child present), else 0

Special cases:

• Odd node with single child x: compress(x, 0, key) (i.e., the missing sibling is zero).
• Singleton tree (only one element in the tree): still apply one compression round.
• Merkle Paths need only sibling hashes: left/right direction is inferred from the binary decomposition of the leaf index, so you don’t transmit direction flags.

2. Sampling

Sampling request Sampling begins when a proof is requested containing the entropy (also called ProofChallenge). A DataSampler instance is created for a specific slot and then used to produce Sample records (see the Sample type in the Data Models section).

The sampler needs:

• slotIndex: the index of the slot being proven. This is fixed when the DataSampler is constructed.
• entropy: public randomness (e.g., blockhash).
• nSamples: the number of cells to sample.

Derive indices The sampler derives deterministic cell indices from the challenge entropy and the slot commitment:

idx = H(entropy || slotRoot || counter) mod nCells

where counter = 1..nSamples and H is the Poseidon2 sponge (rate = 2) with 10* padding. The result is a sequence of distinct indices in [0, nCells), identical for any honest party given the same (entropy, slotRoot, nSamples).

Generate per-sample data

• Fetch the cellData via the BlockStore and builder, and fetch the stored cell -> block, block -> slot, slot -> dataset Merkle paths. Note that cell -> block can be build on the fly and slot -> dataset can be reused for all samples in that slot.

Collect Proof Inputs The DataSampler collects the ProofInputs required for the zk proof system which contains the following:

• entropy: the challenge randomness.
• datasetRoot: the root of the dataset Merkle tree.
• slotIndex: the index of the proven slot.
• slotRoot: the Merkle root of the slot tree.
• nCellsPerSlot: total number of cells in the slot.
• nSlotsPerDataSet: total number of slots in the dataset.
• slotProof: the slot -> dataset Merkle path.
• samples: a list where each element is:
  • cellData: the sampled cell encoded as field elements.
  • merklePaths: the concatenation (cell -> block) || (block -> slot).

These ProofInputs are then passed to the prover to generate the succinct ZK proof.

3. Generating a ZK proof To produce a zk storage proof, Codex uses a pluggable proving backend. In practice we use Groth16 over BN254 (altbn128) with circuits written in Circom. The Prover withProofInputs calls the backend to create a succinct proof, and (optionally) verifies it locally before submission. The prover require the following dependencies:

• Circuits (Circom): codex-storage-proofs-circuits
  Circom implementations of Codex’s proof circuits (targets Groth16 over BN254). The circuits contain the logic to ensure knowledge of the randomly sampled set of cells (see circuit specification below).

• Circom to Nim — nim-circom-compat
  Nim bindings that load compiled Circom artifacts, serialize ProofInputs, and expose a small API (prove, verify) that returns/consumes CircomProof.

• FFI backend (Arkworks Groth16) — circom-compat-ffi Rust library with a C ABI used to run Arkworks Groth16 proving and verification on BN254.

ZK Circuit Specification

Circuit parameters (compile-time constants)

MAXDEPTH     # max depth of slot tree (block -> slot)
MAXSLOTS     # max number of slots in dataset (slot -> dataset)
CELLSIZE     # cell size in bytes (e.g., 2048)
BLOCKSIZE    # block size in bytes (e.g., 65536)
NSAMPLES     # number of sampled cells per challenge (e.g. 100)

Public inputs

datasetRoot  # root of the dataset (slot -> dataset)
slotIndex    # index of the slot being proven
entropy      # public randomness used to derive sample indices

Witness (private inputs)

slotRoot     # root of the slot (block -> slot) tree
slotProof    # Merkle path for slot -> dataset
samples[]:   # one entry per sampled cell:
  cellData     # the sampled cell encoded as field elements
  merklePaths  # (cell -> block) || (block -> slot) Merkle path

Constraints (informal) For each sampled cell, the circuit enforces:

1. Cell hashing: recompute the cell digest from cellData using the Poseidon2 sponge (rate=2, 10* padding).
2. Cell -> Block: verify inclusion of the cell digest in the block tree using the provided cell -> block path.
3. Block -> Slot: verify inclusion of the block digest in the slot tree using the block -> slot path.
4. Slot -> Dataset: verify inclusion of slotRoot in dataset tree using slotProof.
5. Sampling indices: recompute the required sample indices from (entropy, slotRoot, NSAMPLES) and check that the supplied samples correspond exactly to those indices.

Output (proof)

• Groth16 proof over BN254: the tuple (A \in \mathbb{G}_1,\; B \in \mathbb{G}_2,\; C \in \mathbb{G}_1), referred to in code as CircomProof.

Verification

• The verifier (on-chain or off-chain) checks the proof against the public inputs using the circuit’s verifying key (derived from the CRS generated at setup).
• On EVM chains, verification leverages BN254 precompiles.

6. Dependencies

The proving module relies on the components and libraries below.

• Marketplace / Storage Contracts: request/proof events.
• BlockStore: Content-addressed store used by the proving module to read raw block bytes and store/fetch Merkle proofs (paths).
• Manifest: Dataset metadata and commitment references (CIDs) required to construct trees.
• Merkle Tree and Hash Function:
  • Codex Safe Merkle tree implementation.
  • Poseidon2 sponge for cell hashing.
  • Poseidon2 keyed 2-to-1 compression for Merkle tree layers.
• Nim bindings for Circom:
  • nim-circom-compat
  • Arkworks Groth16 FFI
• Groth16 Circuits: Groth16 circuits witten in codex-storage-proofs-circuits.

7. Data Models

Builder

SlotsBuilder*[T, H] = ref object of RootObj
  store: BlockStore
  manifest: Manifest # current manifest
  strategy: IndexingStrategy # indexing strategy
  cellSize: NBytes # cell size
  numSlotBlocks: Natural
    # number of blocks per slot (should yield a power of two number of cells)
  slotRoots: seq[H] # roots of the slots
  emptyBlock: seq[byte] # empty block
  verifiableTree: ?T # verification tree (dataset tree)
  emptyDigestTree: T # empty digest tree for empty blocks

Sampler

DataSampler*[T, H] = ref object of RootObj
  index: Natural
  blockStore: BlockStore
  builder: SlotsBuilder[T, H]

Prover

Prover* = ref object of RootObj
    backend: AnyBackend
    store: BlockStore
    nSamples: int

Sample

  Sample*[H] = object
    cellData*: seq[H]
    merklePaths*: seq[H]

Public Input

  PublicInputs*[H] = object
    slotIndex*: int
    datasetRoot*: H
    entropy*: H

Proof Input

  ProofInputs*[H] = object
    entropy*: H
    datasetRoot*: H
    slotIndex*: Natural
    slotRoot*: H
    nCellsPerSlot*: Natural
    nSlotsPerDataSet*: Natural
    slotProof*: seq[H]
    samples*: seq[Sample[H]]

Proof

pub struct Proof {
    pub a: G1,
    pub b: G2,
    pub c: G1,
}