# TenSEAL: A Library for Encrypted Tensor Operations Using Homomorphic Encryption

arXiv: https://arxiv.org/abs/2104.03152 · HTML: https://mlsanity.com/paper/2104.03152/

## Key facts

- Cited by 52 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 22 references that are in the map.
- First posted on arXiv 7 Apr 2021.

## Builds on (most cited first, 4 of 4)

- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Gazelle: A Low Latency Framework for Secure Neural Network Inference](https://mlsanity.com/paper/1801.05507/) (2018)
- [nGraph-HE: A Graph Compiler for Deep Learning on Homomorphically Encrypted Data](https://mlsanity.com/paper/1810.10121/) (2018)
- [nGraph-HE2: A High-Throughput Framework for Neural Network Inference on Encrypted Data](https://mlsanity.com/paper/1908.04172/) (2019)

## Built on by (most cited first, 30 of 52)

- [FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning System](https://mlsanity.com/paper/2303.10837/) (2023)
- [FLASHE: Additively Symmetric Homomorphic Encryption for Cross-Silo Federated Learning](https://mlsanity.com/paper/2109.00675/) (2021)
- [FRAG: Toward Federated Vector Database Management for Collaborative and Secure Retrieval-Augmented Generation](https://mlsanity.com/paper/2410.13272/) (2024)
- [Split HE: Fast Secure Inference Combining Split Learning and Homomorphic Encryption](https://mlsanity.com/paper/2202.13351/) (2022)
- [A First Look At Efficient And Secure On-Device LLM Inference Against KV Leakage](https://mlsanity.com/paper/2409.04040/) (2024)
- [Deploying Convolutional Networks on Untrusted Platforms Using 2D Holographic Reduced Representations](https://mlsanity.com/paper/2206.05893/) (2022)
- [When Federated Learning meets Watermarking: A Comprehensive Overview of Techniques for Intellectual Property Protection](https://mlsanity.com/paper/2308.03573/) (2023)
- [Catch-Only-One: Non-Transferable Examples for Model-Specific Authorization](https://mlsanity.com/paper/2510.10982/) (2025)
- [Personalized and privacy-preserving federated heterogeneous medical image analysis with PPPML-HMI](https://mlsanity.com/paper/2302.11571/) (2023)
- [Split Without a Leak: Reducing Privacy Leakage in Split Learning](https://mlsanity.com/paper/2308.15783/) (2023)
- [Privacy-Preserving Diffusion Model Using Homomorphic Encryption](https://mlsanity.com/paper/2403.05794/) (2024)
- [HyFedRAG: A Federated Retrieval-Augmented Generation Framework for Heterogeneous and Privacy-Sensitive Data](https://mlsanity.com/paper/2509.06444/) (2025)
- [FheFL: Fully Homomorphic Encryption Friendly Privacy-Preserving Federated Learning with Byzantine Users](https://mlsanity.com/paper/2306.05112/) (2023)
- [Dual Defense: Enhancing Privacy and Mitigating Poisoning Attacks in Federated Learning](https://mlsanity.com/paper/2502.05547/) (2025)
- [Effect of Homomorphic Encryption on the Performance of Training Federated Learning Generative Adversarial Networks](https://mlsanity.com/paper/2207.00263/) (2022)
- [Hyperdimensional Computing as a Rescue for Efficient Privacy-Preserving Machine Learning-as-a-Service](https://mlsanity.com/paper/2310.06840/) (2023)
- [Federated Learning is Better with Non-Homomorphic Encryption](https://mlsanity.com/paper/2312.02074/) (2023)
- [QuanCrypt-FL: Quantized Homomorphic Encryption with Pruning for Secure Federated Learning](https://mlsanity.com/paper/2411.05260/) (2024)
- [Exploiting Unstructured Sparsity in Fully Homomorphic Encrypted DNNs](https://mlsanity.com/paper/2503.09184/) (2025)
- [AEGIS: Scaling Long-Sequence Homomorphic Encrypted Transformer Inference via Hybrid Parallelism on Multi-GPU Systems](https://mlsanity.com/paper/2604.03425/) (2026)
- [HE-MAN -- Homomorphically Encrypted MAchine learning with oNnx models](https://mlsanity.com/paper/2302.08260/) (2023)
- [PackVFL: Efficient HE Packing for Vertical Federated Learning](https://mlsanity.com/paper/2405.00482/) (2024)
- [CipherFace: A Fully Homomorphic Encryption-Driven Framework for Secure Cloud-Based Facial Recognition](https://mlsanity.com/paper/2502.18514/) (2025)
- [Encrypted Vector Similarity Computations Using Partially Homomorphic Encryption: Applications and Performance Analysis](https://mlsanity.com/paper/2503.05850/) (2025)
- [PPFPL: Cross-silo Privacy-preserving Federated Prototype Learning Against Data Poisoning Attacks](https://mlsanity.com/paper/2504.03173/) (2025)
- [Technical Evaluation of a Disruptive Approach in Homomorphic AI](https://mlsanity.com/paper/2506.11954/) (2025)
- [Privacy-Preserving Chest X-ray Classification in Latent Space with Homomorphically Encrypted Neural Inference](https://mlsanity.com/paper/2506.15258/) (2025)
- [Efficient Privacy-Preserving Recommendation on Sparse Data using Fully Homomorphic Encryption](https://mlsanity.com/paper/2509.03024/) (2025)
- [OmniFed: A Modular Framework for Configurable Federated Learning from Edge to HPC](https://mlsanity.com/paper/2509.19396/) (2025)
- [A Secure and Private Distributed Bayesian Federated Learning Design](https://mlsanity.com/paper/2602.20003/) (2026)

## Abstract (arXiv)

Machine learning algorithms have achieved remarkable results and are widely applied in a variety of domains. These algorithms often rely on sensitive and private data such as medical and financial records. Therefore, it is vital to draw further attention regarding privacy threats and corresponding defensive techniques applied to machine learning models. In this paper, we present TenSEAL, an open-source library for Privacy-Preserving Machine Learning using Homomorphic Encryption that can be easily integrated within popular machine learning frameworks. We benchmark our implementation using MNIST and show that an encrypted convolutional neural network can be evaluated in less than a second, using less than half a megabyte of communication.