When the internet was first being built, it was not yet known how to perform arbitrary computations on encrypted data. This gap shaped the basic architecture of the internet and influences how billions of people interact with computers in the cloud.
However, in 2009, Gentry reported a viable construction of a Fully Homomorphic Encryption (FHE) scheme, which exploits additive and multiplicative homomorphisms of ideal lattices. More recent advances, such as Halevi et al.'s RNS Variant of the BFV Homomorphic Encryption scheme have sharply reduced the computational complexity (and noise accumulation) of these techniques.
Today, in 2021, for the first time in the history of the internet, it’s possible to build cryptographic privacy into the apps, servers, and browsers that we use to communicate and transact. Our mission at Enya is to help rebuild the internet, by making it easy to use advanced privacy-preserving techniques in millions of sites, servers, and apps all around the world.
Enya Launches First Privacy-Preserving Symptom Mapping Solution to Help Scientists Fight Covid-19 And Businesses Re-open Safely. Try out FeverIQ today.
The Enya SDK makes privacy-preserving computation easy to add to existing websites or mobile applications. For example, the technology can be used to protect the privacy of millions of people all around the world, as they help scientists discover new COVID-19 symptoms on the FeverIQ.com site. The same API can be used to securely compute on user-provided data to provide personalized scores, risk estimates, and products.
Visit Quick Start to learn more about the technology platform and About FeverIQ to learn more about how they are deriving insights from secured data that never leave the phone.
The Enya SDK makes privacy-preserving computation effortless and straightforward. To compute on secured, private data, first configure your algorithm and then copy the following code into your app:
import EnyaSMC from 'enyasmc';
/* Configure the SDK */
EnyaSMC.Configure({
CLIENT_TOKEN: "f7edB8a8A4D7dff85d2CB7E5",
algo_name: "sample_algo"
});
/* Provide the client's data */
const answers = [0.6, 2, 0, 42.6];
EnyaSMC.Input.apply(this, answers)
/* Compute on the data */
EnyaSMC.Linear().then((function(result){
console.log(result)
}))
/* Successful output: {secure_result: score, status_code: 200} */
Visit Quick Start to learn more.