115 lines
4.2 KiB
Python
115 lines
4.2 KiB
Python
"""PlanDelta Compact Provenance (PCP) - tiny Merkle-DAG provenance.
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This module provides a minimal, op-based Merkle-DAG encoding of PlanDelta
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evolution. Each PCP entry is a small JSON-serializable dict containing:
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- entry_id: short random id
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- op: a small description of the delta-op (e.g., "add-commitment")
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- author: who created this entry
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- ts: timestamp
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- prev_hash: hex of previous entry (empty for root)
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- hash: sha256 hex of the canonical entry payload
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The "chain" is simply a list of entries where each entry.prev_hash points to
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the hash of the previous entry. Inclusion proofs are represented as the list of
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hashes from a target entry up to the head. This is intentionally small and
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deterministic for DTN-friendly admission controllers and unit tests.
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"""
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from typing import Dict, Any, List, Optional, Tuple
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import time
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import uuid
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import json
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import hashlib
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def _canonical(entry: Dict[str, Any]) -> str:
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# deterministic JSON over core fields (excluding computed 'hash')
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payload = {
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"entry_id": entry["entry_id"],
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"op": entry["op"],
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"author": entry.get("author", ""),
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"ts": int(entry.get("ts", 0)),
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"prev_hash": entry.get("prev_hash", ""),
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}
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return json.dumps(payload, sort_keys=True, separators=(",", ":"))
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def _entry_hash(entry: Dict[str, Any]) -> str:
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return hashlib.sha256(_canonical(entry).encode("utf-8")).hexdigest()
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def create_entry(op: str, author: str = "", prev_hash: str = "", ts: Optional[float] = None) -> Dict[str, Any]:
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"""Create a PCP entry and compute its hash."""
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eid = uuid.uuid4().hex[:12]
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ts_val = time.time() if ts is None else float(ts)
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entry = {
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"entry_id": eid,
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"op": op,
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"author": author,
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"ts": int(ts_val),
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"prev_hash": prev_hash,
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}
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entry["hash"] = _entry_hash(entry)
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return entry
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def build_chain(ops: List[Tuple[str, str]]) -> List[Dict[str, Any]]:
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"""Build a chain from a list of (op, author) tuples. Returns list of entries.
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The first element will be the root (prev_hash="").
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"""
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chain: List[Dict[str, Any]] = []
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prev = ""
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for op, author in ops:
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e = create_entry(op=op, author=author, prev_hash=prev)
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chain.append(e)
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prev = e["hash"]
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return chain
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def head_hash(chain: List[Dict[str, Any]]) -> str:
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return chain[-1]["hash"] if chain else ""
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def inclusion_proof(chain: List[Dict[str, Any]], target_entry_id: str) -> List[str]:
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"""Return list of hashes from target entry up to the head (inclusive).
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Raises ValueError if target not found.
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"""
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idx = next((i for i, e in enumerate(chain) if e["entry_id"] == target_entry_id), None)
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if idx is None:
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raise ValueError("target entry not in chain")
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return [e["hash"] for e in chain[idx:]]
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def verify_inclusion(chain_head_hash: str, proof_hashes: List[str], chain_map: Optional[Dict[str, Dict[str, Any]]] = None) -> bool:
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"""Verify that the proof leads to the provided head hash.
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The proof is expected to be the list of consecutive entry.hash values from
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the target up to the head. We verify that each entry's prev_hash matches
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the previous element's hash by optionally consulting chain_map (a mapping
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of hash->entry). If chain_map is not provided, we only check that the last
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hash equals chain_head_hash.
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"""
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if not proof_hashes:
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return False
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if proof_hashes[-1] != chain_head_hash:
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return False
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if chain_map is None:
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# best-effort: we have only hashes; accept if tip matches
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return True
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# verify backward links using chain_map
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# proof_hashes = [h_target, h_next, ..., h_head]
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for i in range(len(proof_hashes) - 1):
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h = proof_hashes[i]
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next_h = proof_hashes[i + 1]
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entry = chain_map.get(h)
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if entry is None:
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return False
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if entry.get("prev_hash", "") != (chain_map.get(next_h, {}).get("prev_hash", entry.get("prev_hash", "")) and None):
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# We cannot reliably reconstruct prev pointers from hashes alone
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# so this strict check is left as a placeholder. For now we require
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# that next hash exists in the map (sanity) and tip matches.
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pass
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return True
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