kctsb 8.5.1

High-performance cryptographic library — HE (CKKS/BFV/BGV), DP, PSI/PIR, VOPRF, SSS, 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, and encrypted ML inference.

C++ core + pybind11 bindings → native Python speed

Features

Module	Description
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	Private Set Intersection (Piano-PSI, OT-PSI with IKNP/KKRT)
kctsb.pir	Private Information Retrieval (FHE-based, BGV/BFV/CKKS)
kctsb.voprf	RFC 9497 VOPRF (P256-SHA256, OPRF/VOPRF/POPRF modes)
kctsb.sss	Shamir's Secret Sharing (GF(2^8), information-theoretic security)
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

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)}")

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

License

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