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TFHE is an open-source library for fully homomorphic encryption, distributed under the terms of the Apache 2.0 license.The underlying scheme is described in best paper of the IACR conference Asiacrypt 2016: “Faster fully homomorphic encryption: Bootstrapping in less than 0.1 seconds”, presented by Ilaria Chillotti, Nicolas Gama, Mariya Georgieva and Malika Izabachène.
Check out the latest TFHE implementation: TFHE-rs on Github.
TFHE-rs is a pure Rust implementation of TFHE for booleans and integers FHE arithmetics over encrypted data. The library is meant for developers and researchers who want full control over what they do with TFHE, while not concerning themselves with the low level implementation.
TFHE-rs aims at being the new reference implementation for TFHE by extending the usual scheme possibilities, including up-to-date security parameters and state-of-the-art features.
- TFHE Deep Dive - Part I - Ciphertext types
https://www.zama.ai/post/tfhe-deep-dive-part-1
- TFHE Deep Dive - Part II - Encodings and linear leveled operations
https://www.zama.ai/post/tfhe-deep-dive-part-2
- TFHE Deep Dive - Part III - Key switching and leveled multiplications
https://www.zama.ai/post/tfhe-deep-dive-part-3
- TFHE Deep Dive - Part IV - Programmable Bootstrapping
https://www.zama.ai/post/tfhe-deep-dive-part-4
- Asiacrypt 2016: Best paper Award
https://eprint.iacr.org/2016/870.pdf
- iDASH 2019: 1st place
http://www.humangenomeprivacy.org
- TFHE-rs (state of the art):
https://github.com/tfhe-rs
- TFHE lib:
https://github.com/tfhe/tfhe
- Concrete:
https://github.com/zama-ai/concrete
- Experimental-TFHE lib:
https://github.com/tfhe/experimental-tfhe
- CuFHE:
https://github.com/vernamlab/cuFHE
- MK-TFHE:
https://github.com/ilachill/MK-TFHE
- Cingulata:
https://github.com/CEA-LIST/Cingulata
- Google FHE:
https://github.com/google/fully-homomorphic-encryption
- Note: as this list might not be up-to-date, you can check out all recent TFHE related papers here:
https://eprint.iacr.org/search?q=tfhe
- Improved Programmable Bootstrapping with Larger Precision and Efficient Arithmetic Circuits for TFHE:
https://eprint.iacr.org/2021/729
- Faster fully homomorphic encryption: Bootstrapping in less than o.1 seconds:
https://eprint.iacr.org/2016/870.pdf
- Fast Homomorphic Evaluation of Deep Discretized Neural Networks:
https://eprint.iacr.org/2017/1114.pdf
- Faster Packed Homomorphic Operations and Efficient Circuit Bootstrapping for TFHE:
https://eprint.iacr.org/2017/430.pdf
- Towards efficient and secure Fully Homomorphic Encryption and cloud computing (Ilaria’s PhD thesis):
https://ilachill.github.io/
- Chimera: a unified framework for B/FV, TFHE and HEAAAn fully homomorphic encryption and predictions for deep learning:
https://eprint.iacr.org/2018/758.pdf
- New Techniques for Multi-value Input Homomorphic Evaluation and Applications:
https://eprint.iacr.org/2018/622.pdf
- Multi-Key Homomorphic Encryption from TFHE:
https://eprint.iacr.org/2019/116.pdf
- Onion Ring ORAM: Efficient Constant Bandwidth Oblivisous RAM from (Leveled) TFHE:
https://eprint.iacr.org/2019/736.pdf
- TFHE: Fast Fully Homomorphic Encryption over the Torus:
https://eprint.iacr.org/2018/421
- New Challenges for Fully Homomophic Encryption:
https://ppml-workshop.github.io
- CONCRETE: Concrete Operates oN Ciphertexts Rapidely by Extending TfhE:
https://homomorphicencryption.org
- Programmable Bootstrapping Enables Efficient Homomorphic Inference of Deep Neural Networks:
https://eprint.iacr.org/2021/091.pdf
- Improved Programmable Bootstrapping with Larger Precision and Efficient Arithmetic Circuits for TFHE:
https://eprint.iacr.org/2021/729.pdf
- Guide to Fully Homomorphic Encryption over the [Discretized] Torus:
https://eprint.iacr.org/2021/1402
- Parameter Optimization & Larger Precision for (T)FHE:
https://eprint.iacr.org/2022/704.pdf