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Related work and alternatives

This page situates sigma-rangeproof among the other zero-knowledge options reachable from Python, so the design choice is visible rather than assumed. The honest summary: the pure-Python ZK ecosystem is thin. Most of what exists falls into one of three buckets — primitive libraries you build on, research code, or Python bindings over a Rust/C proving system. A small, dependency-free library that does exactly one proof is an unusual niche, which is the gap this package fills.

Note

The ZK ecosystem moves quickly and packages come and go. Treat the specifics below as a starting map, not a current inventory — check maintenance status and security posture yourself before depending on anything.

Direct alternatives for range proofs

  • zkbp (JPMorgan Chase). Python bindings over a Rust implementation on the BN254 pairing curve; offers Sigma protocols and Bulletproof-style range proofs, and pairs with their PADL ledger kit. Faster and more compact than a pure-Python O(n) proof, and EVM-adjacent. Caveats: it ships a compiled dependency, is labelled "research code … not for production," and BN254's security was revised down to roughly 100 bits after improved tower-number-field-sieve attacks (2016), which is why newer work prefers BLS12-381. Reach for it (or arkworks directly) if you need aggregated, log-size proofs at volume or on-chain verification.

  • zksk + petlib. An academic "Zero-Knowledge Swiss Knife" for composing Sigma protocols (discrete-log representations, AND/OR), built on petlib's OpenSSL-backed elliptic-curve arithmetic. Closest in spirit to this library's building blocks, but it is a toolkit for assembling proofs rather than a packaged range proof, depends on OpenSSL bindings, and is lightly maintained.

  • Bulletproofs ports. Several pure-Python Bulletproofs implementations exist on GitHub as references; as of writing none is a maintained, packaged PyPI dependency. The reference implementations everyone actually uses are in Rust — dalek-cryptography/bulletproofs (over ristretto255) and the arkworks stack — reached from Python only through custom bindings or WASM.

Building blocks (not proof systems)

These give you the group arithmetic to build Sigma protocols on, but no proof on their own:

  • py_ecc (Ethereum Foundation) — pure Python BN254/BLS12-381 with pairings; correct but slow, used as a reference.
  • petlib, fastecdsa, coincurve — elliptic-curve / secp256k1 primitives (the latter two are native-backed and fast).
  • noknow — a Schnorr-style NIZK for authentication (prove you know a secret), not range proofs; mentioned because it is often the first hit when searching "python zero knowledge."

Heavier proof systems with Python bindings

When the statement is an arbitrary computation rather than "a number is in a range," you move to general-purpose SNARK/STARK systems. These are powerful and production-grade, but they bring a circuit compiler and a native toolchain:

  • pysnark — write Python, get a zk-SNARK over an arithmetic circuit (libsnark / snarkjs backends). Research-grade.
  • ezkl — halo2-based proving of computations and ML inference (zkML), Rust under Python bindings.
  • Cairo / starknet.py — the STARK ecosystem; prove a Cairo program, verify on StarkNet.

Most of the serious options above are ultimately a Python skin over the same few Rust crates — arkworks, dalek (curve25519/ristretto, Bulletproofs), and Merlin transcripts. This library deliberately uses none of them: its transcript is a simpler SHA-256 analog of Merlin, and its group is a prime field so that Python's C-accelerated pow does the heavy lifting and no native build is needed.

Where this library sits

sigma-rangeproof trades the things the native systems are good at — small proofs, aggregation, raw speed, general circuits — for a narrow set of properties they mostly cannot offer together:

  • Zero dependencies, pure Python, no build step — installs and vendors anywhere, no wheels per platform, trivial to read end to end.
  • One job, done plainly — prove value ≥ threshold over a Pedersen commitment, with a normative spec and published test vectors.
  • Auditable by a single person in a sitting, which is the whole point for a threshold-gate use case where a bug should degrade gracefully rather than be catastrophic.

If your constraints are "server-side, occasional, small ranges, no native dependency, easy to audit," this is the right tool. If they are "huge volume, tiny proofs, or on-chain," one of the Rust-backed options above is, and this page is your pointer to them.