Q That people, um, use is differential privacy. Can you, can you maybe just, um, educate us on the nuances?
A So, you know, the arsenal of tools that we have, so to speak, for, for enabling private analytics and analytics on sensitive data, is that there are a number of techniques. Uh, differential privacy is one, which is, you know, kind of like a real high-level hand-waving version of it, is that you add a bunch of noise to the data, so you can, Kind of, um, uh, obfuscate the actual privacy sensitive information. Uh, the challenge with that, it actually doesn't work very well if you need really fine-grained analysis as you would, for example, uh, genome-wide association studies. So it, you know, uh, there are several other techniques such as secure multi-party computing where multiple participants each have a share of the data and they run some sort of, uh, joint computation, but it's very bandwidth heavy. Uh, and, and there are a couple other different techniques. And, and there is a broad set of Privacy enhancing, privacy protecting analytics technologies, of which Homework for encryption is one of them, and it does solve some problems nicely, and of course there's others that it doesn't, but it just so happens to be that this is what we focus on.
AI assessment note: “differential privacy is one, which is... you add a bunch of noise to the data”