{"categories":[{"label":"Cryptography","url":"https://skillfed.io/packages/category/security-cryptography/2"}],"enrichment":{"capability":"TenSEAL performs homomorphic encryption operations on tensors, enabling arithmetic and linear algebra on encrypted data without decryption using the BFV and CKKS encryption schemes.","skillfed_tags":["homomorphic-encryption","privacy-preserving-ml","secure-computation"],"use_cases":["Train machine learning models on encrypted data without exposing raw inputs to the server.","Perform encrypted inference on sensitive data in cloud environments where the service provider cannot see plaintext.","Compute dot products and matrix operations on encrypted vectors for privacy-preserving analytics.","Implement encrypted convolution operations for image processing on confidential datasets.","Build secure multi-party computation workflows where encrypted tensors are processed without intermediate decryption."],"what_it_does":"TenSEAL is a Python library that wraps Microsoft SEAL to perform arithmetic operations directly on homomorphically encrypted tensors. It supports two encryption schemes: BFV for integer vectors and CKKS for real-number vectors. The library exposes element-wise addition, subtraction, and multiplication on encrypted-encrypted and encrypted-plain vector pairs, as well as dot products and matrix multiplication, all without decrypting the data.\n\nThe implementation prioritizes efficiency by delegating most operations to C++ while maintaining a Python API for ease of use. It is built on top of numpy for its single runtime dependency. TenSEAL is designed for privacy-preserving machine learning workflows where sensitive data must remain encrypted during computation\u2014for example, training logistic regression models or performing convolutions on encrypted data. The library also exposes the complete SEAL API under `tenseal.sealapi` for advanced use cases.","worth_installing":"Yes, if you need to perform computations on encrypted tensors for privacy-preserving machine learning. The library is actively maintained, has no known vulnerabilities, and provides prebuilt wheels for common platforms. Install friction is moderate due to C++ compilation requirements, but wheels eliminate build overhead for standard Python versions (3.11\u20133.14). The Apache-2.0 license is permissive. Not worth installing if you do not require homomorphic encryption or if your platform is not covered by prebuilt distributions."},"id":"tenseal","links":{"html":"https://skillfed.io/packages/tenseal","md":"https://skillfed.io/packages/tenseal.md","pypi":"https://pypi.org/project/tenseal/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-04","license_spdx":"Apache-2.0","license_treatment":"permissive","name":"tenseal","python_support":"supports_current","summary":"A Library for Homomorphic Encryption Operations on Tensors"},"popularity":{"monthly_downloads":81846,"position":14196,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.3.17"}