# OpenMined/TenSEAL

A library for doing homomorphic encryption operations on tensors

Repository: https://github.com/OpenMined/TenSEAL
Canonical: https://ross.abutalabs.com/products/tenseal
Language: C++
License: Apache-2.0
License Family: permissive
Topics: tensor, homomorphic-encryption, cryptography, deep-learning, python, cpp, microsoft-seal, encryption, docker-image, hacktoberfest
Last push: 2026-08-22T21:26:13+00:00

## Health v2 (maintenance only)
Score: 87/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 99, release rhythm 64, longevity 100
- inputs: {"age_days": 2412, "days_push": 11, "days_rel": 30, "gap_med": 336.0, "n_releases_24m": 3}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1033, forks 174 (observed 2026-08-28T04:03:18.315502+00:00)

## What it is
TenSEAL is a Python library (with a C++ core) for performing homomorphic encryption operations on tensors, built on top of Microsoft SEAL. It supports BFV and CKKS schemes for encrypting vectors of integers and real numbers and computing on them without decryption.

## Use cases
- perform arithmetic on encrypted vectors without decrypting them
- encrypt machine learning model inputs with homomorphic encryption
- compute dot products and matrix multiplication on encrypted data
- run privacy-preserving inference on sensitive data
- use CKKS encryption for real-number tensors in Python
- access the full Microsoft SEAL API from Python

## When to choose
- you need to compute on encrypted numeric data while keeping it confidential
- you want a Python-friendly API over Microsoft SEAL for encrypted tensor math
- you are building privacy-preserving machine learning or secure aggregation pipelines

## When to avoid
- you need general-purpose encryption like TLS or file encryption rather than computation on ciphertexts
- performance is critical, as homomorphic encryption carries heavy computational overhead
- you need non-numeric data types or schemes beyond BFV/CKKS

## Facets
- artifact type: library
- maturity: active
- function: cryptography, machine-learning, math, serialization
- domain: machine-learning, privacy, deep-learning
- platform: python, cpp, windows, cross-platform
- tags: homomorphic-encryption, bfv, ckks, microsoft-seal, encrypted-tensors, privacy-preserving-computation, cryptography, linux, macos

## Member repositories
- OpenMined/TenSEAL (main) score 87

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:18.315502+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T07:06:38.025752+00:00, confidence not recorded.
  - readme: https://github.com/OpenMined/TenSEAL (fetched 2026-08-28T04:03:18.315502+00:00, sha 6b004e3082d8)
- Data as of 2026-08-30T08:39:29.467469+00:00.