regaudit-fhe 0.0.2

Depth-tracked regulatory audit primitives for future FHE-CKKS execution.

regaudit-fhe

Encrypted regulatory audit primitives for AI systems.

A small Python library of six audit operations that can be evaluated on encrypted inputs under fully-homomorphic encryption (CKKS, multiplicative depth six) without bootstrapping. Designed so a regulated AI vendor can run mandatory audits — fairness, drift, calibration, provenance, disagreement, survival concordance — without ever exposing raw labels, predictions, protected attributes, or training data.

Maintained by VaultBytes Innovations Ltd. Licensed under AGPL-3.0-or-later — see LICENSE.

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What it is for

┌──────────────┐      encrypted inputs       ┌────────────────┐
│ Regulated    │ ──────────────────────────► │ regaudit-fhe   │
│ AI vendor    │   (labels, preds, PHI,      │ d=6 CKKS audit │
│ (the client) │    protected attrs)         │ primitives     │
└──────────────┘                             └──────┬─────────┘
       ▲                                            │
       │                                            │ encrypted
       │   sealed envelope                          │ aggregate
       │   (JSON + receipt)                         │ scalars only
       │                                            ▼
       │                                     ┌────────────────┐
       └───────────────────────────────────► │ Regulator /    │
                                             │ external       │
              decrypt + verify receipt       │ auditor        │
                                             └────────────────┘

Two audiences are served by the same surface area:

Audience	Role	What they run
Client	Audited entity (vendor / operator).	audit_* primitives, regaudit-fhe audit ....
Regulator	External or in-house auditor.	verify_receipt(...), regaudit-fhe verify ....

A run produces an audit envelope (JSON) with a SHA-256 receipt. The client archives it, ships it to the regulator, or both. The regulator recomputes the receipt to detect tampering between issuance and review.

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The six primitives

Module	API	Depth	Use case (technical evidence supporting…)
egf_imss	audit_fairness	4	NYC LL144, EU AI Act §10/§15, Colorado AI Act, CFPB workflows.
etk_fpa_hbc	audit_provenance	3	EU AI Act §10, 21 CFR Part 11, GDPR §22, HIPAA workflows.
esc_cia	audit_concordance	4	FDA SaMD oncology PCCP, EU AI Act §15, EMA workflows.
ecp_qssp	audit_calibration	3	FDA SaMD UQ, EU AI Act §15, ISO/IEC 23053, UNECE WP.29 workflows.
ew1_cdsf	audit_drift	3	EU AI Act §15, FDA SaMD PCCP, Basel III workflows.
ecmd_jps	audit_disagreement	5	OCC SR 11-7, EU AI Act §15, FDA SaMD PCCP workflows.

Compliance scope and disclaimer. regaudit-fhe produces technical evidence — encrypted scalars, signed envelopes, parameter-set hashes, depth-budget attestations — that may support compliance workflows in the jurisdictions above. It does not constitute legal compliance, conformity assessment, regulatory acceptance, or a recognised audit. Read COMPLIANCE.md for the binding scope statement and the "what-it-does-NOT-prove" mapping per regulation.

Each primitive's depth budget — the number of multiplicative levels it consumes inside the d=6 CKKS circuit — is shown above. All six fit comfortably under six, leaving headroom for downstream commit-and-verify chaining.

Depth budget visualisation (each ▮ = 1 level)

    primitive             0 1 2 3 4 5 6
    ─────────────────────────────────────
    audit_calibration     ▮ ▮ ▮ . . . .   3 of 6
    audit_provenance      ▮ ▮ ▮ . . . .   3 of 6
    audit_drift           ▮ ▮ ▮ . . . .   3 of 6
    audit_fairness        ▮ ▮ ▮ ▮ . . .   4 of 6
    audit_concordance     ▮ ▮ ▮ ▮ . . .   4 of 6
    audit_disagreement    ▮ ▮ ▮ ▮ ▮ . .   5 of 6

Each primitive's full specification, including its algorithm, depth breakdown, and security analysis, is in docs/specs/.

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Install

pip install regaudit-fhe

The plaintext model that runs the depth-budgeted slot-vector circuits (used for testing, oracles, and CI) requires only numpy>=1.26. The encrypted execution path lives behind the optional [fhe] extra:

pip install regaudit-fhe[fhe]    # adds OpenFHE

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Quick start

import numpy as np
import regaudit_fhe as rf

y_true  = np.array([1, 0, 1, 1, 0, 1, 0, 0], dtype=float)
y_pred  = np.array([1, 0, 1, 0, 0, 1, 1, 0], dtype=float)
group_a = np.array([1, 1, 1, 1, 0, 0, 0, 0], dtype=float)
group_b = 1.0 - group_a

report = rf.audit_fairness(y_true, y_pred, group_a, group_b, threshold=0.1)
print(report.demographic_parity_diff, report.threshold_breached)

envelope = rf.envelope("fairness", report)
print(envelope.to_json())                     # ship this to the regulator

assert rf.verify_receipt(envelope) is True    # regulator-side check

The HTTP server is NOT a privacy boundary by itself. The default execution path is plaintext; the server runs in-process. Encrypted execution requires the [fhe] extra AND key custody held off-host. Read docs/DEPLOYMENT.md and docs/THREAT_MODEL.md before exposing publicly. Without the closed-source companion product, the server cannot mint regulator-trusted envelopes.

Same flow, command line

echo '{"y_true":[1,0,1,1],"y_pred":[1,0,0,0],"group_a":[1,1,0,0],"group_b":[0,0,1,1]}' \
  > input.json

regaudit-fhe audit fairness -i input.json -o envelope.json
regaudit-fhe verify -i envelope.json

regaudit-fhe audit <primitive> --schema prints the JSON shape that each primitive expects.

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Audit envelope

Every audit_* call can be wrapped into a regulator-facing JSON envelope by regaudit_fhe.envelope(...):

{
  "schema": "regaudit-fhe.report.v1",
  "primitive": "fairness",
  "regulations": ["NYC_LL144", "EU_AI_ACT_ART10",
                  "EU_AI_ACT_ART15", "COLORADO_AI_ACT",
                  "CFPB_ALG_DISCRIM"],
  "result": {
    "demographic_parity_diff": 0.083,
    "equal_opportunity_diff": 0.041,
    "predictive_parity_diff": 0.022,
    "threshold_breached": false
  },
  "depth_budget": {"declared": 6, "consumed": 4},
  "issued_at": "2026-04-26T20:30:11.482910+00:00",
  "receipt": {
    "sha256": "9f3c…b4a7",
    "version": "0.0.1"
  }
}

schema and regulations give a regulator the exact citation they need. receipt.sha256 is computed over the canonical JSON of every other field; verify_receipt(env) returns False if anything changed.

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Examples

The examples/ folder ships four end-to-end flows:

File	Flow
01_client_local_audit.py	Internal audit on synthetic data; archive JSON locally.
02_client_to_regulator.py	Build a regulator submission bundle.
03_regulator_verify.py	Verify every envelope inside a submission bundle.
04_cli_roundtrip.sh	Pure CLI: input → audit → verify, no Python knowledge required.

Run any of them after pip install -e .[dev].

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Layout

src/regaudit_fhe/      depth-tracked plaintext model + 6 primitives + reports + CLI
docs/specs/            per-primitive technical specifications
docs/THREAT_MODEL.md   roles, key custody, public surface per primitive
schemas/               JSON Schemas (Draft 2020-12) for every input, output, and the envelope
tests/                 pytest unit, integration, edge-case, property-based, schema, security tests
examples/              client + regulator end-to-end flows
benchmarks/            d=6 CKKS wall-clock + memory benchmarks

JSON schemas

Every primitive input, primitive output, and the audit envelope itself ships with a Draft-2020-12 JSON Schema under schemas/. Auditors and integrators can pin specific schema versions and reject payloads that do not conform.

regaudit-fhe schema --list
regaudit-fhe schema fairness.input
regaudit-fhe schema envelope

The CLI and HTTP server validate every request body against the matching schema before invoking the audit primitive; failures return HTTP 422 / CLI exit-code 2 with a structured pointer to the offending field. Programmatic access:

import regaudit_fhe as rf
rf.list_schemas()                      # all 13 names
rf.load_schema("fairness.input")       # raw schema dict
rf.validate_input("fairness", payload) # raises rf.SchemaError on bad input
rf.validate_envelope(env_dict)         # check a regulator-side envelope

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Real CKKS benchmarks

The [fhe] extra ships a real TenSEAL CKKS backend; its measurements are reproduced from benchmarks/results/SUMMARY.md (machine-readable JSON in benchmarks/results/bench_fhe_<N>.json).

Primitive	N	Slots	Depth obs/decl	Rotations	ct×ct	ct×pt	Runtime	RAM	Max abs err	Threshold flip
fairness	2^14	8 192	1/4	108	0	18	0.28 s	878 MB	4.8 × 10⁻⁷	0.00%
provenance	2^14	8 192	1/3	288	0	48	0.71 s	885 MB	7.9 × 10⁻⁶	0.00%
concordance	2^14	8 192	4/4	0	0	0	0.00 s	887 MB	0	0.00%
calibration	2^14	8 192	0/3	0	0	0	0.01 s	890 MB	0	0.00%
drift	2^14	8 192	2/2	36	3	6	0.18 s	968 MB	2.3 × 10⁻⁶	0.00%
disagreement	2^14	8 192	3/5	0	36	0	0.16 s	977 MB	1.1 × 10⁻⁸	0.00%
fairness	2^15	16 384	1/4	108	0	18	0.58 s	2.7 GB	7.2 × 10⁻⁷	0.00%
provenance	2^15	16 384	1/3	288	0	48	1.52 s	2.7 GB	1.9 × 10⁻⁵	0.00%
drift	2^15	16 384	2/2	36	3	6	0.39 s	2.8 GB	1.1 × 10⁻⁵	0.00%
disagreement	2^15	16 384	3/5	0	36	0	0.35 s	2.7 GB	1.4 × 10⁻⁸	0.00%

Run yourself:

pip install regaudit-fhe[fhe]
python benchmarks/bench_fhe.py --rings 14 15 --reps 3

Add --rings 16 for N = 2^16 (slower; uses several GB of RAM).

The "Threshold flip" column is the rate at which CKKS noise causes the encrypted circuit to disagree with the plaintext circuit on a boolean breach decision over 10–20 trials with inputs sampled near the breach boundary. Zero flips across both rings means CKKS noise does not change a regulatory threshold decision at the audit precision targets.

Maturity and status

Description today: Depth-tracked regulatory audit primitives for future FHE-CKKS execution. Description after the OpenFHE backend lands: FHE-CKKS regulatory audit primitives for privacy-preserving AI system audits.

regaudit-fhe v0.0.1 ships:

• the plaintext SlotVec model with strict depth-budget enforcement,
• a TenSEAL CKKS backend that mirrors the SlotVec algebra and passes end-to-end ciphertext / plaintext equivalence tests,
• the Ed25519-signed audit envelope with canonical-JSON rules, parameter-set hashing, and input commitments,
• JSON Schemas for every input, output, and the envelope itself,
• the hardened HTTP audit server with bearer-token auth, scopes, body- size limit, rate limiting, structured logs, and CORS controls,
• supply-chain controls: Trusted-Publisher PyPI release, Sigstore attestation, CycloneDX SBOM, pip-audit, weekly Dependabot.

The OpenFHE production backend at N = 2^15, calibrated polynomial packs per vertical, KMS-backed signing key chains, and regulator- portal connectors live in the closed-source companion product. Contact [e-mail] for commercial licensing.

Contributions are not accepted — see CONTRIBUTING.md.