regaudit-fhe 0.0.1

Encrypted regulatory audit primitives for AI systems under FHE-CKKS depth-6

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

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

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
tests/                 pytest unit + integration tests
examples/              client + regulator end-to-end flows
benchmarks/            d=6 CKKS wall-clock + memory benchmarks (planned)

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Status

regaudit-fhe is at v0.0.1: the full plaintext model and audit envelope are in place; the OpenFHE backend ([fhe] extra) and a benchmark harness against OpenFHE / Concrete-ML at N = 2^15 are the next milestones.

Contributions welcome under AGPL-3.0. Commercial licensing inquiries: [e-mail].