lkowalcz
Joined 13 years ago
Joined 13 years ago
Here’s a blog post from a former employee at a new startup comparing kdb against some alternative databases for common operations on financial data: https://medium.com/prooftrading/selecting-a-database-for-an-...
I don't think that doing this stifles innovation (In fact, I think requiring crypto innovations to have security justifications is probably better overall for innovation in the field)
From the Wikipedia page for "Neural cryptography", it seems like there's some success in using NN's for cryptanalysis, but not for constructions...
Edit: Do you mean the Google GAN experiment? (https://arxiv.org/pdf/1610.06918v1.pdf) Ahh ok, well at least for this there looks like an attempt at defining a security model (security against some other NN). I don't really believe the security model is realistic (how do we know NN's are really that effective as adversaries?), but at least there is a model, so calling that "encryption" sits somewhat better with me. I'm pretty sure this is not what's being used by Numerai since it seems like it would not result in ciphertexts with the structure necessary to perform ML operations on.
Edit2: Maybe you're right and it is more advanced. In any case, as a crypto nerd I wish they would disclose what they are actually doing / the rationale instead of tantalizingly suggesting that they have made (what would be) a breakthrough in a practical use case of advanced encryption schemes, but not saying how.
I still don't think it's fair to market their method as comparable to Aslett's scheme or "standard" notions of homormorphic/order preserving encryption, no matter how "chill" they are :)
Their (closed-source) method of obfuscating their data apparently does have the property that it preserves the structure of the data, but calling it "structure-preserving encryption" is misleading imo since it risks confusing it with standard notions of encryption and structure-preserving encryption which have much stronger security guarantees.
(Their marketing seems to encourage this conflation by, for example, citing academic advances in standard notions of homomorphic encryption and SPE and implying that these advances have enabled Numerai's technology)
https://medium.com/numerai/encrypted-data-for-efficient-mark...
Universities make much more money off their Masters students, most of whom are international, and pay full tuition (or someone does for them).
Given this "encrypted" X , y dataset, I could easily find the unencrypted version... (even if I don't know 20 or -0.5, this still reveals so much of the structure that I don't believe it provides any real protection against anything except the most lazy attackers)
https://www.cs.columbia.edu/~luke/