Read the latest publications by the people at Zama

want to Join our research team?

See the job openings

SEPTEMBER 2022 — CHES

Guide to Fully Homomorphic Encryption over the [Discretized] Torus

Marc Joye



Read blog post    Read paper   

SEPTEMBER 2022 — SCN

Scooby: Improved multi-party homomorphic secret sharing based on FHE

Ilaria Chillotti, Emmanuela Orsini, Peter Scholl, Nigel Smart, and Barry Van Leeuwen

Read blog post    Read paper   

June 2022 — CSCML

Blind rotation in fully homomorphic encryption with extended keys

Marc Joye and Pascal Paillier

Read paper

December 2021 — ASIACRYPT

Balanced non-adjacent forms



— Marc Joye                                                       

Read paper

December 2021 — ASIACRYPT

Improved programmable bootstrapping with larger precision and efficient arithmetic circuits for TFHE

Ilaria Chillotti, Damien Ligier, Jean-Baptiste Orfila, and Samuel Tap

Read paper

July 2021 — CSCML

Programmable bootstrapping enables efficient homomorphic inference of deep neural networks

— Ilaria Chillotti, Marc Joye, and Pascal Paillier

Read paper

December 2020 — WAHC

CONCRETE: Concrete Operates oN Ciphertexts Rapidly by Extending TfhE

— Ilaria Chillotti, Marc Joye, Damien Ligier, Jean-Baptiste Orfila, and Samuel Tap

Read paper

December 2020 — PPML

New challenges for fully homomorphic encryption


— Ilaria Chillotti, Marc Joye, and Pascal Paillier

Read paper

august 2020 — USENIX

SANNS: Scaling up secure approximate k-nearest neighbors search


Hao Chen, Ilaria Chillotti, Yihe Dong, Oxana Poburinnaya, Ilya Razenshteyn, and M. Sadegh Riazi

Read paper

SEPTEMBER 2022 — CHES

Guide to Fully Homomorphic Encryption over the [Discretized] Torus

— Marc Joye



Read blog post    Read paper    

SEPTEMBER 2022 — SCN

Scooby: Improved multi-party homomorphic secret sharing based on FHE

Ilaria Chillotti, Emmanuela Orsini, Peter Scholl, Nigel Smart, and Barry Van Leeuwen

Read blog post    Read paper   

June 2022 — CSCML

Blind rotation in fully homomorphic encryption with extended keys

Marc Joye and Pascal Paillier

Read paper

MAY 2022 — EUROCRYPT

CoCoA: Concurrent continuous group key agreement

Joël Alwen, Benedikt Auerbach, Miguel Cueto Noval, Karen Klein, Guillermo Pascual-Perez, Krzysztof Pietrzak, and Michael Walter

Read paper

MAY 2022 — FHE.org

Hybrid attacks on LWE and the lattice estimator

Martin Albrecht, Benjamin R. Curtis, and Michael Walter

Read slides

MAY 2022 — FHE.org

Fast, easy, and accessible FHE with Concrete and specialized accelerators

Florent Michel, Thomas De Cnudde, and Agnès Leroy

Read poster

MAY 2022 — FHE.org

Concrete ML: A data-scientist-friendly toolkit for machine learning over encrypted data

Benoît Chevallier-Mames, Jordan Fréry, Arthur Meyre, and Andrei Stoian

Read poster

MAY 2022 — FHE.org


Performance of hierarchical transforms in homomorphic encryption: A case study on logistic regression inference

Pedro G. M. R. Alves, Jheyne N. Ortiz, and Diego F. Aranha

Read poster

January 2022 — CT-RSA

A pairing-free signature scheme from correlation intractable hash function and strong Diffie-Hellman assumption

Benoît Chevallier-Mames

Read paper

December 2021 — ASIACRYPT

Balanced non-adjacent forms




— Marc Joye

Read paper

December 2021 — ASIACRYPT

Improved programmable bootstrapping with larger precision and efficient arithmetic circuits for TFHE

Ilaria Chillotti, Damien Ligier, Jean-Baptiste Orfila, and Samuel Tap

Read paper

NOVEMBER 2021 — ASIACRYPT

Grafting key trees: Efficient key management for overlapping groups

Joël Alwen, Benedikt Auerbach, Mirza Ahad Baig, Miguel Cueto Noval, Karen Klein, Guillermo Pascual-Perez, Krzysztof Pietrzak, and Michael Walter

Read paper

NOVEMBER 2021 — TCC

The cost of adaptivity in security games on graphs

Chethan Kamath, Karen Klein, Krzysztof Pietrzak, and Michael Walter

Read paper

December 2021 — Cell Systems

Ultrafast homomorphic encryption models enable secure outsourcing of genotype imputation

Miran Kim, Arif Harmanci, Jean-Philippe Bossuat, Sergiu Carpov, Jung Hee Cheon, Ilaria Chillotti, Wonhee Cho, David Froelicher, Nicolas Gama, Mariya Georgieva, Seungwan Hong, Jean-Pierre Hubaux, Duhyeong Kim, Kristin Lauter, Yiping Ma, Lucila Ohno-Machado, Heidi Sofia, Yongha Son, Yongsoo Song, Juan Troncoso-Pastoriza, and Xiaoqian Jian

Read paper

July 2021 — CSCML

Programmable bootstrapping enables efficient homomorphic inference of deep neural networks

— Ilaria Chillotti, Marc Joye, and Pascal Paillier

Read paper

NOVEMBER 2020 — J. Math. Cryptol.

The eleventh power residue symbol


— Marc Joye, Oleksandra Lapiha, Ky Nguyen, and David Naccache

Read paper

December 2020 — WAHC


CONCRETE: Concrete Operates oN Ciphertexts Rapidly by Extending TfhE

— Ilaria Chillotti, Marc Joye, Damien Ligier, Jean-Baptiste Orfila, and Samuel Tap

Read paper

December 2020 — PPML

New challenges for fully homomorphic encryption


— Ilaria Chillotti, Marc Joye, and Pascal Paillier

Read paper

august 2020 — USENIX

SANNS: Scaling up secure approximate k-nearest neighbors search


Hao Chen, Ilaria Chillotti, Yihe Dong, Oxana Poburinnaya, Ilya Razenshteyn, and M. Sadegh Riazi

Read paper

We’re building
products to make
FHE programming
fast and easy

Concrete Numpy

Concrete Numpy is an open-source set of tools which aims to simplify the use of fully homomorphic encryption (FHE) for data scientists.

RESOURCES

Explore more content about the Concrete Numpy library

Titanic Competition with Privacy Preserving Machine Learning

A Privacy-Preserving Machine Learning (PPML) solution to the famous ML Titanic challenge using concrete-ml

Read Article

Announcing Concrete ML v0.2

We are announcing the release of Concrete ML as a public alpha. The package is built on top of Concrete Numpy.

Read Article

Privacy-preserving insurance quotes

A tutorial on how to build an FHE-enabled insurance incident predictor.

Read Article

Hummingbird

Concrete Numpy is an open-source set of tools which aims to simplify the use of fully homomorphic encryption (FHE) for data scientists.

RESOURCES

Explore more content about the Hummingbird library

Titanic Competition with Privacy Preserving Machine Learning

A Privacy-Preserving Machine Learning (PPML) solution to the famous ML Titanic challenge using concrete-ml

Read Article

Announcing Concrete ML v0.2

We are announcing the release of Concrete ML as a public alpha. The package is built on top of Concrete Numpy.

Read Article

Privacy-preserving insurance quotes

A tutorial on how to build an FHE-enabled insurance incident predictor.

Read Article