Fully Homomorphic Encryption (FHE) is a cryptographic technology that’s tipped for big things.
Its proponents believe FHE to be genuinely groundbreaking due to its unique ability to permit encrypted data processing without decrypting its contents.
In cryptographic circles, this is the equivalent of having your cake and eating it: the best of both worlds, combining data privacy with secure processing and storage.
Given the value of such a technology, which has been likened to “having a magical glove that allows you to add or remove items from a treasure chest without ever unlocking it,” it’s no surprise that its real-world applications are diverse.
Within industries such as blockchain, telecommunications, and finance, FHE is being put to use to create powerful encrypted applications of all kinds.
Here are just four use cases for Fully Homomorphic Encryption.
Cloud Computing
Businesses and consumers the world over are now reliant on the cloud to host their data and perform computation.
Our most personal stories, images, and precious digital creations are stored in the cloud alongside sensitive company data.
All of this data is encrypted, but that hasn’t prevented vast troves of it from being accessed by attackers.
One of the problems with extant encryption technology is that the data must be decrypted before it can be processed and its contents accessed, introducing a potential attack vector which a sophisticated adversary can exploit.
FHE eliminates this achilles heel, allowing sensitive data to not only be stored in encrypted form but also processed in this state.
As a result, cloud storage becomes much more secure and the type of datasets that can be stored within it expand greatly.
For example, a healthcare provider can store patient records encrypted in the cloud and run queries on the data without revealing any patient information to the cloud service provider.
Digital Voting
One of the reasons why most countries don’t permit digital voting for national elections is due to fears of tampering. In-person voting may be inefficient, but it’s hard to falsify.
FHE has the potential to transform digital voting, using a blockchain framework to record who’s voted without revealing who they’ve voted for.
This is one of the use cases being explored by Fhenix, the first confidentiality-enabling Layer 2 blockchain powered by fully homomorphic encryption.
The FHE implementation created by Fhenix has a number of applications that include supporting private voting for DAOs, making it possible for organizers to gauge total voter participation without disclosing the voting choice of each participant.
This same capability could be utilized for real-world voting, implemented onchain but used to determine major events including governmental and presidential choices.
In an election, voters can submit their encrypted votes to a central server, which can count the votes without decrypting them, ensuring voter anonymity and election integrity.
Enhanced Machine Learning
The AI industry is reliant on running large machine learning (ML) datasets to train models.
The problem is, companies are often restricted from accessing the data they would like for ML purposes due to data-sharing regulations in place restricting its usage.
FHE allows encrypted datasets to be computed, ensuring that ML models can receive the training they require without the underlying information being exposed.
The ability to combine multiple datasets to permit large scale ML training is the key to making real advancements in artificial intelligence.
Using FHE for ML training is one of the primary use cases for FHE advocated by IBM, and as the AI arms race heats up, expect to see FHE routinely mentioned in the same breath as ML.
Financial Analysis
Perhaps one of the most compelling use cases for FHE is for processing financial data.
The unique properties of this cryptographic technology allow sensitive financial data to be analyzed at scale without exposing sensitive data in the process.
For example, banks can calculate credit scores and risk assessments on encrypted customer financial data without revealing the data to unauthorized entities.
The financial sector can also leverage FHE to allow analysts to work on large datasets to conduct complex modeling of things such as economic projections, macro forecasting, fiscal policy development, and financial threat analysis, combining multiple datasets without increasing the risk of a major fallout through data leakage since the data remains encrypted at all times.
From securing messaging apps to IoT sensor data, FHE has a host of applications.
The ability to conduct high-value analytics and data processing, without decrypting data, is a real game-changer, while use cases such as digital voting and ML modeling provide further proof that the future of cryptography is inexorably linked with the future of FHE.
