OpenMined/TenSEAL
A library for doing homomorphic encryption operations on tensors
This library helps data scientists and machine learning engineers perform calculations on numerical data (vectors and matrices) without ever decrypting it. You feed it encrypted numbers, and it performs operations like addition, multiplication, dot products, and matrix multiplication, yielding an encrypted result that you can later decrypt. This is useful for privacy-preserving machine learning or secure data analysis workflows.
1,006 stars. Used by 2 other packages. Available on PyPI.
Use this if you need to perform computations on sensitive numerical data while it remains encrypted, ensuring privacy throughout the analysis or model training process.
Not ideal if your primary goal is speed and you don't require the strict privacy guarantees of homomorphic encryption for your data.
Stars
1,006
Forks
171
Language
C++
License
Apache-2.0
Category
Last pushed
Feb 27, 2026
Commits (30d)
0
Reverse dependents
2
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