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Learner Track: Developer

This learner track assumes you are a developer wanting to learn about FHE. To see other available Learner Tracks, click here.

Table of Contents



Generalities

Fully homomorphic encryption (FHE) allows you to run computations on encrypted data without decrypting it first. As a developer, this means you’re able to write code that doesn’t jeopardize the privacy of your users.

Benefits of using FHE in your applications as a developer can include:


Ideal Use Cases

Homomorphic encryption is ideal for some use cases more than others.


Cloud / Distributed Computing

FHE enables secure computations on encrypted data stored in the cloud without the need to decrypt it. This preserves the confidentiality of sensitive data even when processed by third-party cloud service providers.
Working Examples
More Reading


Data Analysis and Actions

FHE allows for analysis and performing actions on encrypted data without revealing the actual content analysts. For instance, healthcare providers can conduct statistical analysis on confidential patient information without decrypting it, thus maintaining patient privacy.
Working Examples
More Reading


Machine Learning

FHE enables machine learning models to be trained on encrypted data. This is beneficial when data owners are hesitant to share their data for privacy reasons. With FHE, data remains encrypted during the model training process, and only the encrypted model is shared.
Working Examples

Have examples to contribute? Add them here!
More Reading


Accounting / Banking

FHE allows financial institutions to analyze encrypted financial transactions for fraud detection while maintaining client confidentiality.
Working Examples
More Reading


Blind and Private Databases

FHE allows encrypted search and querying of databases without exposing search criteria or database contents, thus ensuring privacy for data owners who want to allow others to search and access information.
Working Examples

Working Examples
More Reading


Threat model

Homomorphic encryption is not a silver bullet for all use cases. As with all cryptography, how practical or safe it is to use is ultimately dependent on the use case, implementation, library, and underlying scheme.



Encryption Strength The security of homomorphic encryption relies on the strength of the underlying encryption scheme. If the encryption scheme is compromised or broken, the encrypted data and computations could be exposed.
Implementation Flaws Like any cryptographic system, the implementation of homomorphic encryption algorithms and protocols may introduce vulnerabilities. Implementation flaws, such as programming errors or side-channel attacks, could potentially compromise the security of the encrypted data. It's important to have your code audited, use libraries that are audited, and also schemes that have been publicly tested and accepted by the community as safe.
Metadata Leakage While homomorphic encryption protects the content of the data, it does not hide the metadata associated with the encrypted computations. Metadata leakage could enable attackers to infer information about the encrypted data, such as its size, access patterns, or the type of computations being performed.
Key Management Homomorphic encryption schemes typically require the management of encryption keys. Adequate key management practices, including key generation, distribution, storage, and revocation, are crucial to maintaining the security of the encrypted data.


More Reading





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Constraints

There are several constraints regarding the implementation of homomorphic encryption-based solutions. Each of these constraints needs to be considered carefully when implementing an FHE-based solution.

Choosing Parameters

The target homomorphic encryption scheme needs to be initialized with parameters which are used throughout the computation. These parameters determine everything performance related: the security level, the efficiency of homomorphic operations, the size of key material, the input (and output) precision, how noise grows throughout the computation, and when a special operation called bootstrapping needs to be performed. We consider each of these issues below, one-by-one. 


---
title: A Competing Trade-off of Constraints
---
flowchart TB
    Speed <--> Precision
    Speed <--> Security
		Speed <--> ObjectSizes
		Speed <--> Noise
    Noise <--> Precision
    Noise <--> Security
		Noise <--> ObjectSizes
		ObjectSizes <--> Security
		ObjectSizes <--> Precision
		Precision <--> Security

Approximate vs Exact computation

FHE schemes can be categorised in many ways. One of the most important distinctions is whether an FHE scheme is exact or approximate. In the first case computations are typically considered over the integers and correctness of output is guaranteed. In the latter case, a small error is incurred during computation, so that the output of a homomorphic function evaluation, such as computing `f(x)`, contains a small error: `f(x) + e`.


Input Precision

FHE schemes typically work over restricted input domains (for example: the integers modulo `p`). This means that when implementing an algorithm in FHE, it is important to consider whether the algorithm needs to be adapted to work over this domain. As an example, consider the `max()` function on real numbers: suppose that we want to compute the max of 0.001 and 8.764. Certain FHE schemes work modulo `p` (as mentioned above), so the first step is to figure out how to map our inputs, and the respective max algorithm, into this modular domain.
Further Reading


Output Precision

For approximate schemes, such as CKKS, the homomorphic encryption scheme carries an implicit error which impacts the message. The parameters can be adapted to guarantee a specific output precision, but this is important to note when considering implementing solutions in CKKS.
Further Reading
  • Part 2 of a blog series on CKKS by Daniel Huynh (OpenMined) in 2020.
  • Introduction to CKKS (Approximate Homomorphic Encryption) video by Yongsoo Song (SNU) in 2020.


Speed

The speed of FHE-based operations is constantly improving. However, there is still a large slow-down compared to plaintext operations, and it is important to consider this when getting started. There are a variety of open-source implementations of various algorithms implemented in FHE, and looking at benchmarks for these applications can give a good indication of where FHE is at.
Further Reading


Object Sizes

When data is encrypted using an FHE scheme, there is an expansion factor which needs to be considered. As an example, in the TFHE scheme, to encrypt somewhere in the order of 1-8 bits, a ciphertext will be of size 5 kilobytes. Evaluation keys, which are required for the server to carry out homomorphic operations over ciphertexts, can be very large. In particular, all evaluation keys stored on the server are just collections of ciphertexts, and can be in the order of tens of megabytes (or even over a gigabyte, in certain cases). This means that there is a high bandwidth requirement on FHE-based solutions.


Noise and Bootstrapping

As homomorphic operations are being computed, the level of noise contained in the utilised ciphertext grows. The noise level must not be allowed to overflow a specific bound, or errors can be introduced into computations. The process used to clear this noise is called bootstrapping, and, depending on the scheme, has a varying level of efficiency.
Further Reading
  • A blogpost on bootstrapping by Nigel Smart (Zama) in 2023.
  • A blogpost on bootstrapping by Yuriy Polyakov and Ahmad Al Badawi (Duality) in 2023.


Security

It is important that the homomorphic encryption scheme is parameterised correctly to ensure a sufficient level of security. The security is related a problem called Learning with Errors, and we need to choose cryptographic parameters such that this problem is hard to solve. Luckily, there are many FHE-based tools which take the problem of parameter selection away from the user. Moreover, most FHE libraries typically have default parameter sets for which the security level has already been determined.
Further Reading
  • Wikipedia page on the Learning with Errors problem.
  • Stackexchange answer to: What is a security parameter?
  • A blogpost on estimating the security of FHE schemes by Zama in 2021.
  • Taking the problem out of the hands of the user: Zama’s security curves implementation by Zama in 2023.





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Schemes and Libraries

Schemes

Schemes are the mathematical algorithms that are used for the underlying FHE operations on data. There are several major schemes implemented in modern libraries, each taking a different approach that has their own benefits and drawbacks on what types of operations can be done, the depth of calculations, and the resulting latency.
  • BFV by Fan Junfeng; Vercauteren Frederik
  • BGV by Z. Brakerski; C. Gentry, V. Vaikuntanathan
  • CKKS by Cheon Jung Hee; Kim Andrey; Kim Miran; Song Yongsoo
  • FHEW by Leo Ducas; Daniele Micciancio
  • TFHE by Ilaria Chillotti; Nicolas Gama; Mariya Georgieva; Malika Izabachene


Libraries

Libraries are the software developers use to perform FHE operations easily, similarly to how one might use the `libsodium` library for basic cryptographic operations. Each library is designed for working with different schemes, some with multiple schemes and the ability to switch between them as needed.
  • Concrete - TFHE Compiler that converts python programs into FHE equivalent by Zama
  • Concrete ML - Privacy Preserving ML framework built on top of Concrete by Zama
  • HEaaN - C++ FHE library implementing CKKS scheme that supports bootstrapping and GPU-acceleration by Crypto Lab
  • HElib - BGV scheme with bootstrapping and the Approximate Number CKKS scheme by IBM
  • Lattigo - Lattice-based multiparty homomorphic encryption library in Go by Tune Insight
  • OpenFHE - Production-ready implementations of all major FHE schemes by Duality Tech
  • SEAL - C++ FHE library implementing BFV and CKKS schemes by Microsoft
  • TFHE-rs - A pure Rust implementation of the TFHE scheme for boolean and integer arithmetics over encrypted data by Zama


Choosing the right scheme/library

Choosing which library/scheme is right for you can be confusing at first, but here are some considerations that help in the decision making process:

In the following section, you’ll have a chance to use existing sample code and write your own code. If at that point you’re still unsure which library is right for you or have deeper questions about it the libraries and schemes, consider joining the FHE.org community on Discord and asking the community directly. We’re here to help!





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Start Coding

The best way to get started in FHE is to look at Tutorials. In particular: trying to read them, understand the code, and then extend the examples with your own code.

Below is a list of all resources from Tutorials and Examples found on various libraries repositories and some diagram to let you pick one library to start with. Note that every libraries have their own capabilities that are not covered here.

Select an FHE library

By Language

stateDiagram
	Prog_lang: Where to start
    Prog_lang: by language
    Prog_lang --> C++
    Prog_lang --> Rust
    Prog_lang --> Go
    Prog_lang --> Python
	C++ --> OpenFHE
	C++ --> SEAL
    C++ --> HEAAN
    C++ --> HELIB
	Go --> LattiGo
	TFHErs: TFHE-rs
	Rust --> TFHErs
	Python --> pyFHEL
	Python --> Concrete

By Scheme

stateDiagram
    Scheme: Where to start
    Scheme: by FHE scheme
    Scheme --> BFV
    Scheme --> BGV
    Scheme --> CKKS
    Scheme --> TFHE
    BFV --> OpenFHE
    BGV --> OpenFHE
    CKKS --> OpenFHE
    TFHE --> OpenFHE
    BFV --> SEAL
    BGV --> SEAL
    CKKS --> SEAL
    TFHE --> Concrete
    TFHErs: TFHE-rs
    TFHE --> TFHErs

Tutorials

Here is a list of examples and tutorials from each libraries, with some keywords (prefixed with a #) to highlight the language, library, scheme and category they belong to.

Concrete

Lattigo

This section is pending updates to the links.

HEAAN

HELIB

OpenFHE

PyFHEL

SEAL

TFHE-rs





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