A summary of the talk at the OpenMined Privacy Conference 2020
Key Note Speakers
 Mariya Georgieva : Director of Security Innovation at Inpher.
 Nicolas Gama : Chief Scientist at Inpher
Video Link
https://www.youtube.com/watch?v=nn2fFpO4p9Q
What is TFHE ?
TFHE: Fast Fully Homomorphic Encryption over the Torus
TFHE is an opensource library for fully homomorphic encryption, distributed under the terms of the Apache 2.0 license.
TFHE is a C/C++ library which implements a very fast gatebygate bootstrapping, based on [CGGI16] and [CGGI17]. The library allows to evaluate an arbitrary boolean circuit composed of binary gates, over encrypted data, without revealing any information on the data.
Use Cases
 Medicine  Find a Cure against COVID or Cancer
 Machine Learning on Genomic Data
 Physics/Astronautics  Predict Trajectories
In some cases, it may not be possible to run computations on plain text, as the text may not be available in a single location. For general machine learning, models must be trained. Combining datasets can result in more accurate models
The primary goal of secret computing is to run an arbitrary function over secret or private data in a public environment. This is what we call privacypreserving computation.
Main Privacy Preserving Techniques
 Anonymization (Weakest Technique)
 Differential Privacy
 Federated Learning
 Fully Homomorphic Encryption (FHE)
 Multi Party Computation (MPC)
 Secure Enclave (Hardware)
Homomorphic Encryption
What is it?
A cryptosystem is homomorphic if and only if its decryption is a morphism
Decrypt(ab)=Decrypt(a)Decrypt(b)
where * is sum, product, NAND, etc
What are the goals ?
 Publicly operate on ciphertexts without decryption
 Be endtoend semantically secure
 Secure against honest but curious adversaries
HES Community
 An Open Consortium of Industry, Government and Academia to standardize Homomorphic encryption
 Regular Meetings (12 Per Years)
 Four White Papers on
 Security
 Applications
 Standards
 Schemes (BFV/BGV/CKKS/FHEW/TFHE)
Open Source Libraries

TFHE Open Source Library  Faster Bootstrapping.

SEAL OpenSource Library  Library from Microsoft which supports the BFV and CKKS schemes.

HElib OpenSource Library  Library from IBM which supports the BGV and CKKS schemes.
The TFHEChimera Library
Features :
 One very "simple" FHE Boolean API
 "Advanced" API featuring various computation models:
* SIMD Additions, Rotations
* Deterministic (Weighted) Automata
* Chain of Lookup Tables
The Gate Bootstrapping API
Public API v1.0
 Keygen secret and cloud keysets
 Encrypt, Decrypt with secret keyset
 Boots, Gate: CST, AND, OR, XOR, NOT, Mux
 Serialization : Load, Save
Yao's Millionare Problem (1982)
The Comparison Circuit
Homomorphic circuit  full circuit
Gate Bootstrapping: Summary
TFHE Supports other computational models
Some of the computational models supported are
 SIMD Additions + Secret Shifts  Native Operations
 Packing, Unpacking, linear maps  Public and private functional keyswitches with small keys
 Weighted Automata  Good for tropical algebra (max,+) and arithmatic circuits
 LUT with Vertical Packing  Rapidly evaluates complex circuits
Application of THFE
Medicine/Genomic
 Predictive Healthcare
 Finding the right dosage for a cure
 Secure Genotype Imputation
 Understanding Complex Diseases : GWAS
Analyst
Goal : Test the associates between genotypes and phenotypes
 In order to identify generic variants associated with a trait
 Powerful approach for understanding complex diseases (Diabetes, Heart Abnormalities, Parkinson & Crohn Disease, COVID19)
Genomic Service Provider
 Server stores an encrypted database from study participants
 Train models on the encrypted data