pypaillier

Additively homomorphic Paillier encryption: a Rust implementation on top of GMP, bound to Python through pyo3.

Add encrypted numbers without decrypting them:

import paillier

pub, sec = paillier.generate_keypair(2048)

blobs = [bytes(b) for b in paillier.encrypt_many(pub, [1.5, 2.25, -0.75])]
total = paillier.add_many(pub, blobs)      # adding CIPHERTEXTS

paillier.decrypt(sec, total)               # 3.0

Whoever does the adding never sees the terms. Whoever holds the private key sees only the sum.

pip install pypaillier

Wheels for CPython 3.10–3.13, manylinux_2_17. No compiler and no system GMP required. The distribution is pypaillier; the import name is paillier.

📖 Documentation

Before you use it

This is not textbook Paillier. The scheme is the Damgård–Jurik variant with a short exponent, c = (1 + m·n) · hs^r mod n². Ciphertext indistinguishability therefore rests on an additional short-exponent assumption, not on DCRA alone.

Constant time is not claimed as a whole-library property. The three timing channels found in encryption are closed: two are guarded by tests that measure a slope against the secret, and the third — the cache address — cannot be, because it changes neither the answer nor the time measurably; it is guarded by a check on the shape of the code. Key generation is a prime search and is not constant time at all.

Both are explained in full, with the measurements, in the documentation.

Numbers

2048-bit key, medians:

operation cost
encryption, all cores 6800+ ops/s
encryption, one core 1066 ops/s
addition 7.1 µs per term
decryption 3.9 ms
key generation 2.2–3.3 s
ciphertext 512–513 B

Reproduce with python benches/measure.py.

Accuracy

Fixed point, rounding to nearest. The scale is configurable and travels inside the ciphertext, so decryption reads it from there and addition refuses a batch that mixes scales:

blobs = paillier.encrypt_many(pub, values, scale_pow10=12)

The default is 10^8. See Encoding and scale.

Building from source

pip install maturin
maturin build --release

Needs stable Rust. GMP is built and linked statically.

Licences

The sources are MIT OR Apache-2.0, at your option.

The built wheels additionally contain GMP, statically linked through rug, and therefore fall under LGPL-3.0+ as a whole. For a binary with no LGPL code inside, build from the source distribution against your own GMP: pip install --no-binary pypaillier pypaillier.

Full breakdown, including how the LGPL §4 conditions are met: NOTICE.md.

Project links

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Key dates

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Newer release available (0.7.0)

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Credits

Author: Kirill Groshenkov

GitHub Statistics

Data verified by PyPI on Aug 27, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

(MIT OR Apache-2.0) AND LGPL-3.0-or-later
View SPDX License List

Requires

Python >=3.10

Tags

paillier homomorphic-encryption cryptography federated-learning privacy

Classifiers

Report project as malware

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