[lkowalcz]

The encryption here is being done by "adding 20" / "multiplying by -0.5"?

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)

[BickNowstrom]

It is a toy example to show that a form of homomorphic encryption is possible, without going Fully Homomorphic Encryption.

And simple linear transforms on already anonymized features are not so easy to reverse engineer as you may think. Just try it on a few datasets from UCI.

[lkowalcz]

Ah ok, sure. I wouldn't call something like a linear transform on anonymized features "encryption" (more like obfuscation?), but I guess it's good marketing in that it lets them associate with the "recent advances in [real] homomorphic encryption"

[BickNowstrom]

If you desire something more one-way, consider PCA, random projections, feature expansions (with something like Random Bits Regression), hashing, or the last hidden layer activations of your best in-house neural net. Then combine these approaches for good measure.

Agreed on the clever marketing, but at least they put their money (expensive dataset) where their mouth is (release it to reverse engineers the world over).

Fully Homomorphic Encryption challenges would be interesting, but it would disqualify our current state-of-the-art algorithms, and reduce the playing field to a handful of people who know how to write algo's that work with Fully Homomorphic Encryption (if any competitor at all is allowed to work on this, and not too busy working for the NSA).