cipherexplain 0.5.1

CipherExplain SDK — register models and get encrypted SHAP explanations via the CipherExplain API

CipherExplain Python SDK

Register your own models and get encrypted SHAP explanations via the CipherExplain API.

Install

pip install cipherexplain

Dependencies installed automatically: httpx, numpy, scikit-learn.

Optional: local FHE mode

For client-side CKKS encryption (fhe_mode='ckks') install the fhe extra:

pip install 'cipherexplain[fhe]'

This adds OpenFHE so the SDK can encrypt inputs locally and decrypt results without the server ever seeing plaintext.

Quick start

from cipherexplain_sdk import CipherExplainClient, extract_spec

client = CipherExplainClient(api_key="vb_...")

# 1. Train locally — nothing leaves your machine
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
model  = LogisticRegression().fit(scaler.fit_transform(X_train), y_train)

# 2. Extract weights only (no pickle, no training data)
spec = extract_spec(model, "my_model", feature_names=["age", "income", "score"],
                    scaler=scaler)

# 3. Register with the API
client.register(spec)

# 4. Get SHAP explanations — pass raw values, scaler is applied server-side
result = client.explain_raw("my_model", x_raw)
print(result["shap_values"])    # per-feature attributions
print(result["prediction"])     # 0.0–1.0 probability

Supported model types

Tree ensembles (new)

RandomForestClassifier, GradientBoostingClassifier, DecisionTreeClassifier — and their regressor variants — are now fully supported. Use serialize_model from cipherexplain_eval to build the registration payload:

from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.preprocessing import StandardScaler
import cipherexplain_eval as ce

# Train locally
rf = RandomForestClassifier(n_estimators=100).fit(X_train, y_train)

# Serialize to JSON (no pickle, no training data)
payload = ce.serialize_model(
    rf,
    feature_names=["age", "income", "score"],
    model_id="my_rf",
)

# Register with the API (use the raw API or SDK client)
client.register(payload)

# Explain — fhe_mode is ignored for tree models, plaintext KernelSHAP runs
result = client.explain("my_rf", x)
print(result["shap_values"])

GradientBoostingClassifier works the same way — serialize_model captures the learning rate and initial estimator needed for correct probability reconstruction.

Linear classifiers

Weights serialised to JSON — model must be described by a coefficient matrix and intercept vector.

Framework	How
sklearn LogisticRegression	extract_spec(model, ...)
sklearn LinearSVC	extract_spec(model, ...)
Any sklearn-compatible object with coef_ / intercept_	extract_spec(model, ...)
PyTorch nn.Linear	extract_spec(layer, ...)
PyTorch pure-linear nn.Sequential	extract_spec(seq, ...)
TensorFlow / Keras Dense (no activation)	from_weights(layer.get_weights()[0].T, layer.get_weights()[1], ...)
JAX / statsmodels / R / anything else	from_weights(coef, intercept, ...)

All supported model types

model_type	Class	SHAP method
logistic_regression	sklearn.LogisticRegression	KernelSHAP (FHE-simulated)
linear_svc	sklearn.LinearSVC	KernelSHAP (FHE-simulated)
decision_tree	sklearn.DecisionTreeClassifier/Regressor	KernelSHAP (plaintext)
random_forest	sklearn.RandomForestClassifier/Regressor	KernelSHAP (plaintext)
gradient_boosting	sklearn.GradientBoostingClassifier/Regressor	KernelSHAP (plaintext)

TensorFlow / Keras example

from cipherexplain_sdk import from_weights

# Keras model with a single Dense output layer
import numpy as np
w, b = keras_model.layers[-1].get_weights()  # w shape: (n_features, n_classes)
spec = from_weights(
    coef=w.T,          # transpose to (n_classes, n_features)
    intercept=b,
    model_id="keras_model",
    feature_names=[...],
    classes=[0, 1],
)
client.register(spec)

JAX / statsmodels / R example

from cipherexplain_sdk import from_weights

# Pass coefficient array and intercept directly
spec = from_weights(
    coef=[[0.42, -0.17, 0.93]],   # shape (1, n_features) for binary
    intercept=[-0.31],
    model_id="my_model",
    feature_names=["f1", "f2", "f3"],
    classes=[0, 1],
    scaler_mean=[0.0, 0.0, 0.0],  # optional: embed scaler for /explain_raw
    scaler_scale=[1.0, 1.0, 1.0],
)
client.register(spec)

Client reference

client = CipherExplainClient(api_key="vb_...", base_url="https://cipherexplain.vaultbytes.com")

Model management

Method	Description
client.register(spec)	Register a model (spec from extract_spec or from_weights)
client.list_models()	List all models available to your key
client.delete(model_id)	Delete a registered model and free its slot

Slot limits: free = 1 model · developer = 10 · enterprise = unlimited

Explanations

Method	Description
client.explain(model_id, features)	SHAP explanation — features must be pre-scaled
client.explain_raw(model_id, features)	SHAP explanation — raw values, auto-scaled server-side

Both return:

{
    "prediction": 0.74,          # model probability
    "base_rate": 0.31,           # baseline (training set average)
    "shap_values": [...],        # per-feature attribution (sum ≈ prediction - base_rate)
    "feature_names": [...],
    "metadata": {...}
}

Reports & account

Method	Description
client.report(benchmark_data)	Generate PDF audit report — returns raw bytes, save as .pdf
client.usage()	Monthly quota: used / remaining for explain and oracle
client.rotate_key()	Issue new key, deactivate current. Models migrate automatically.
client.health()	API health check — no key required
client.load_demo_model()	Load the demo credit model (credit_model)

Generating a PDF audit report

import json

# Load benchmark results from the FHE Testing Oracle
with open("oracle_benchmark.json") as f:
    benchmark = json.load(f)

pdf_bytes = client.report(benchmark)

with open("audit_report.pdf", "wb") as f:
    f.write(pdf_bytes)

Requires Developer or Enterprise tier.

Key rotation

result = client.rotate_key()
print(result["new_key"])         # vb_... — save this immediately
print(result["models_migrated"]) # all your models move automatically

# Your old key is now inactive — create a new client
client = CipherExplainClient(api_key=result["new_key"])

Error handling

All methods raise httpx.HTTPStatusError on API errors. Check exc.response.status_code:

import httpx

try:
    client.register(spec)
except httpx.HTTPStatusError as exc:
    if exc.response.status_code == 401:
        print("Missing X-API-Key")
    elif exc.response.status_code == 403:
        print("Invalid key or tier restriction (e.g. PDF reports need Developer+)")
    elif exc.response.status_code == 404:
        print("Model not found")
    elif exc.response.status_code == 409:
        print("Model ID already registered — delete it first or use a different ID")
    elif exc.response.status_code == 422:
        print("Validation error:", exc.response.json()["detail"])
    elif exc.response.status_code == 429:
        print("Quota exceeded — model slot limit or monthly call limit reached")
    else:
        raise

Tier limits

	Free	Developer	Enterprise
SHAP API calls / month	50	5,000	50,000
Oracle runs / month	3	500	5,000
Model slots	1	10	Unlimited
PDF audit reports	—	Yes	Yes

Get an API key

vaultbytes.com/cipherexplain