Privacy-Preserving Computation.
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.

Now 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 around the world.

In this introduction, we'll focus on secure multi-party computation, which is one of two major approaches we support.  
Multi-Party Computation.
Imagine you are a doctor who studies obesity and heart attack risk. One of the first things you might like to know are age demographics across particular regions and countries. You could simply try to ask several million people for their date of birth but most people would be reluctant to tell you. Among other uses, banks use this information to verify a person's identity. Would you give your date of birth to someone who just contacted you by email? Even if you trusted that person, you might still be inclined to round that number down a few years, as most people prefer to seem younger.

Here is another way – the doctor could ask everyone to add a random number between (-100) to (+100) to their age and submit the result. So if your age is 27 and your choice of random number is -12, you would subtract 12 from 27 and send the result, 15, to the doctor.
The doctor would then not know your age, but she can still determine something very useful, which is the distribution of ages within the population. To do this, the doctor would average over many responses. Similar to how the mean displacement of a diffusing piece of dust is zero, the random terms would tend to cancel, gradually revealing a good estimate of the population’s true age structure.

This approach works well for research but there is an obvious problem – in a typical medical context, a patient expects their doctor to tell them something about their health and not just make abstract statements about global demographics.  

Let’s add a few twists to the above approach. Here is a simplified example of secure multiparty computation.
Enya Technology Diagram
Imagine a doctor has developed a new algorithm for estimating cardiac risk. Let’s say it’s a simple addition, such as risk = age + 3. The patient, Alice, may wish to keep her age (= 27) secret and the doctor may wish to protect her algorithm (risk = age + 3). As shown in the figure, these two parties could agree to do the following. First, they could both subtract a random number from their secret (Step 1) and then share the differences (Step 2). Next, they could each add their random number to the other party’s share (Step 3) and then exchange the results of those calculations (Step 4). Finally, one (or both) parties could add the shares, revealing the patient’s risk (= 30) without, at any point, transmitting either the patient’s age or the doctor’s secret.

Mathematically, how does this work?

If you kept track of all the terms, you see that risk = (age – rp) + rd + (wd - rd) + rp = age + (rp - rp) + (rd - rd) + wd.
The middle two terms evaluate to zero, simplifying the expression to the desired result, risk = age + wd.

Essentially, both parties inject noise into a communications protocol, the noisy signal undergoes previously agreed-upon linear mathematical operations, and then, the noise is removed at the very end. Since both parties only disclose/share the sums of their noise terms (rp and rd, respectively) is what protects the secrets.

This simplified example omits details required to make such systems work in practice – most obviously, in this toy scheme with only one secret per side, the patient can immediately obtain the doctor’s secret, wd, by inverting the algorithm (risk – 27 = 3). As soon as the secrets on both sides become more complex, such as by involving more than one term on each side, it becomes exceedingly difficult for either party to learn (or even estimate) the other party’s secrets. Multiplication requires additional steps such as mathematically-paired random numbers called Beaver Triples. Here is the original 1991 publication on Beaver Triples (Efficient Multiparty Protocols Using Circuit Randomization).

If you have read to this point, you might be interested to learn more, or, to come join us in Palo Alto to build a foundation for secure and private computation.

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