kctsb 9.0.0

High-performance cryptographic library — HE (CKKS/BFV/BGV), DP, PSI/PIR, VOPRF, SSS, MPC, PQ encoding, and encrypted ML inference. C++ core with Python bindings.

kctsb — Knight's Cryptographic Trusted Security Base

High-performance cryptographic library with Python bindings for homomorphic encryption, differential privacy, private set intersection, PIR, MPC, Product Quantization (PQ) encoding, and encrypted ML inference.

C++ core + pybind11 bindings → native Python speed

Features

Module	Description
kctsb	v9.0.0 — stable hardening release with Debug/Release Werror builds, CSPRNG-backed HCC/MPC/ZK/SSE/OPE/FHE binding randomness, and synchronized PyPI/pybind11 metadata
kctsb.crypto	v8.6.9 — atomic primitives: AES-GCM (AES-NI), ChaCha20-Poly1305, SHA-256/512/3 (SHA-NI), HMAC, HKDF, constant-time CSPRNG, secure_compare, token_bytes, token_hex; hand-written PEP 561 type stubs for all public symbols
kctsb.ckks	CKKS approximate homomorphic encryption (encode, encrypt, add, multiply, rotate)
kctsb.bfv	BFV scale-invariant FHE for integer arithmetic
kctsb.bgv	BGV leveled homomorphic encryption with Galois rotations
kctsb.dp	Differential privacy (Laplace, Gaussian mechanisms, budget tracking)
kctsb.psi	v8.6.10 — Private Set Intersection with domain-separated SHA-256 tokens, constant-time token equality, CSPRNG sampling, and fail-closed PIR provider shims
kctsb.pir	Private Information Retrieval (FHE-based, BGV/BFV/CKKS)
kctsb.ecc_blind_sign	Clause Blind Schnorr signatures (FPS20) — ROS-resistant unlinkable tokens, public verifiable, AGM+ROM secure
kctsb.voprf	RFC 9497 VOPRF (P256-SHA256, OPRF/VOPRF/POPRF modes) — deterministic blinded PRF
kctsb.sss	Shamir's Secret Sharing (GF(2^8), information-theoretic security)
kctsb.mpc	Secure MPC primitives and benchmarks (Rep3 batch secret sharing, Half-Gates GC)
kctsb.encode	v8.6.9 — Product Quantization training, encoding, decoding, ADC lookup, and ADC top-k for private vector query search (PVQS)
kctsb.hcc	Homomorphic Confidential Computing (6 privacy scenarios)
kctsb.upsi	Unbalanced PSI + Symmetric Searchable Encryption
kctsb.core	Hardware acceleration detection (AVX2/AVX512/AES-NI/CUDA)
kctsb.ml	Encrypted ML inference (EncryptedTensor, neural network layers, pipeline)

Quick Start

pip install kctsb

Atomic Crypto Primitives (v8.6.9)

Hardware-accelerated primitives (AES-NI / SHA-NI). Requires Python 3.10+.

from kctsb.crypto import (
    AESGCM, ChaCha20Poly1305,
    sha256, sha512, sha3_256,
    hmac_sha256, hmac_sha512, hkdf_sha256,
    random_bytes, random_uint32, random_uint64, random_range,
    secure_compare, token_bytes, token_hex,
)

# AES-256-GCM (AES-NI accelerated)
key = AESGCM.generate_key(256)     # bit length
nonce = random_bytes(12)
aead = AESGCM(key)
ct = aead.encrypt(nonce, b"hello", b"aad")
pt = aead.decrypt(nonce, ct, b"aad")

# SHA-NI accelerated hashing
digest = sha256(b"payload")         # 32-byte bytes
tag    = hmac_sha256(key, b"msg")   # 32-byte bytes

# Constant-time CSPRNG (platform RtlGenRandom / getrandom)
secret = token_bytes(32)
hexstr = token_hex(16)

# HKDF-SHA256
okm = hkdf_sha256(ikm=secret, salt=b"salt", info=b"context", length=64)

Product Quantization Encoding (v8.6.9)

Compress high-dimensional feature vectors into compact PQ codes and run ADC top-k search. This module is designed for PVQS-style private vector retrieval pipelines where the online private query only carries compact candidate/key handles.

import numpy as np
from kctsb import encode

vectors = np.random.default_rng(20260508).normal(size=(4096, 512)).astype(np.float32)
query = vectors[0]

codebooks = encode.train_pq(vectors, subquantizers=32, centroids=64, max_iterations=8)
codes = encode.encode_pq(vectors, codebooks)
lookup = encode.adc_lookup(query, codebooks)
indices, distances = encode.adc_topk(codes, lookup, top_k=10)

CKKS Homomorphic Encryption

from kctsb import CKKSContext

ctx = CKKSContext(poly_modulus_degree=8192, coeff_modulus_levels=5, scale_bits=40)
sk = ctx.keygen()
pk = ctx.public_key(sk)

pt = ctx.encode([1.0, 2.0, 3.0])
ct = ctx.encrypt(pk, pt)
ct_sum = ctx.add(ct, ct)
result = ctx.decrypt_decode(sk, ct_sum)
print(result[:3])  # [2.0, 4.0, 6.0]

Differential Privacy

from kctsb.dp import DPMechanism, DPBudget

noise = DPMechanism.laplace(sensitivity=1.0, epsilon=0.1)
noisy_data = DPMechanism.add_noise([1.0, 2.0, 3.0], sensitivity=1.0, epsilon=0.1)

budget = DPBudget(total_epsilon=1.0)
budget.consume(0.1)
print(f"Remaining ε: {budget.remaining_epsilon}")

Private Set Intersection

from kctsb._kctsb_core import psi

client_set = list(range(1, 101))
server_set = list(range(50, 151))
piano = psi.PianoPSI()
result = piano.compute(client_set, server_set)
print(f"Intersection size: {len(result.intersection)}")

Multi-Party Computation (MPC)

The Python package now exposes kctsb.mpc for secure MPC primitives and performance benchmarking. Use this module to access Rep3 batch secret sharing APIs and Half-Gates garbled-circuit benchmarks directly from Python.

Shamir's Secret Sharing

from kctsb._kctsb_core import sss

secret = b"my_secret_key_32bytes_long!!!!!"
shares = sss.ShamirSSS.split(secret, threshold=3, num_shares=5)
recovered = sss.ShamirSSS.reconstruct(shares[:3], len(secret))
assert recovered == secret

Hardware Detection

from kctsb._kctsb_core import core

hw = core.detect_hardware()
print(f"AVX2: {hw['avx2']}, AES-NI: {hw['aes_ni']}, CUDA: {hw['cuda']}")
core.print_status()

Building from Source

Developers with the full kctsb source tree can build locally:

# Windows (PowerShell)
cd kctsb
.\scripts\build_python.ps1

# Or manually:
$env:PATH = "C:\msys64\mingw64\bin;$env:PATH"
cmake -B build_release -G Ninja -DCMAKE_BUILD_TYPE=Release -DKCTSB_BUILD_PYTHON=ON
cmake --build build_release --parallel 16
pip install ./python

Requirements

• Python 3.10+
• NumPy >= 1.24
• Windows x64 (pre-built), Linux/macOS (build from source)

Optional Dependencies

pip install kctsb[ml]   # + torch, transformers for encrypted ML
pip install kctsb[dev]  # + pytest, build, twine for development
pip install kctsb[all]  # Everything

Changelog

v9.0.0 (2026-05)

• Hardened project-wide randomness paths to use kctsb platform CSPRNG for HCC blinding/DP noise, MPC MAC checks/triples/shares, ZK scalar sampling, SSE/OPE key and range sampling, and FHE C ABI RNG seeding.
• Verified Debug and Release CMake presets with KCTSB_WARNINGS_AS_ERRORS=ON and synchronized C++/pybind11/PyPI version metadata.
• Updated publishing validation for major-version release-note archives such as docs/releases/V9Release/v9.0.0-release.md.

v8.6.10 (2026-05)

• Hardened PSI/PIR providers with domain-separated SHA-256 tokens and constant-time token/hash equality.
• Replaced PSI sampling and OT seed generation paths with platform CSPRNG calls.
• Removed plaintext CUDA/preprocessing PIR fallbacks; unavailable providers now fail closed.

v8.6.9 (2026-05)

• Added kctsb.encode Product Quantization primitives for PVQS: PQ training, batch encode/decode, ADC lookup, and ADC top-k.
• Added PEP 561 type hints for kctsb.encode and refreshed blind-signature stubs to match the P-256 pybind11 API.
• Added PVQS notebook and protocol design documentation for low-communication private theme/resource retrieval.

v8.6.6 (2026-04)

• Breaking: removed _hashlib / _hmac compat shim objects from kctsb.crypto. Use sha256, hmac_sha256, token_bytes etc. directly.
• Added hand-written PEP 561 type stubs (*.pyi) for all public symbols in kctsb.crypto, kctsb.ckks, kctsb.dp, kctsb.sss and all _kctsb_core sub-modules.
• Secure RAG demo: Phase 0 bootstrap and corpus loader extracted to standalone helper modules.

License

Apache-2.0 — Copyright (c) 2019-2026 knightc