Recently, two key members from Amazon’s AWS Cryptography team, Joan Feigenbaum and Bill Horne, published a very interesting blog-post about how they look at the new paradigm of computing on encrypted data, or as they say “cryptographic computing”.
Via this short post, I would like to highlight some of the amazing quotes, which really put weight behind the importance of this trend. I strongly recommend reading the full article, but here is TL;DR in quotes:
First, both Joan and Bill, illustrate why the trend of computing on encrypted data is an important part of their career:
Joan states: "Over the course of my 40 years as a computer scientist, I’ve worked in many different areas of computer science research, but I’ve always come back to cryptographic computing, because it’s absolutely fascinating and has many practical applications." Bill adds: "There’s a lot of interest from customers right now around cryptographic computing, and so I think that we’re at a really interesting point in time, where this could take off in the next few years. Being a part of something like this is really exciting"
Bill concisely explains some of the very powerful functions of cryptographic computing:
"Cryptographic computing gives organizations a way to train models collaboratively without exposing plaintext data about their customers to each other, or even to an intermediate third party such as a cloud provider like AWS. [..] Instead of using isolation and access control, data is always cryptographically protected, and the processing happens directly on the protected data. [..] In fact, you arguably don’t even need isolation and access control if you are using cryptographic computing, since nothing can be learned by viewing the computation."
Later, Bill lays out the two main techniques currently developed for computing on encrypted data. Roseman Labs focuses on the latter, MPC:
"Two applicable fundamental cryptographic computing techniques are homomorphic encryption and secure multi-party computation. Homomorphic encryption allows for computation on encrypted data. Basically, the idea is that there are special cryptosystems that support basic mathematical operations like addition and multiplication which work on encrypted data. From those simple operations, you can form complex circuits to implement any function you want. [..] Secure multi-party computation is a very different paradigm. In secure multi-party computation, you have two or more parties who want to jointly compute some function, but they don’t want to reveal their data to each other."
About halfway, Joan and Bill clearly argue that they expect a very significant business demand, something Roseman Labs is seeing in its daily work as well.
Joan: "There’s strong motivation to deploy this stuff now, because cloud computing has become a big part of our tech economy and a big part of our information infrastructure. Parties that might have previously managed compute environments on-premises where data privacy is easier to reason about are now choosing third-party cloud providers to provide this compute environment. Data privacy is harder to reason about in the cloud, so they’re looking for techniques where they don’t have to completely rely on their cloud provider for data privacy."
Bill: "Data privacy has become one of the most important issues in security. There is clearly a lot of regulatory pressure right now to protect the privacy of individuals. But progressive companies are actually trying to go above and beyond what they are legally required to do."
Towards the end of the blog, the authors describe some of the exciting analytics and machine learning primitives they are pushing to the secure computation domain, such as regression and classification techniques in machine learning. Something Roseman Labs, with its extensive team of cryptographers, is very proud to also be actively contributing to.