Monday, January 16, 2017

CryptoNets: scoring deep learning on encrypted data

Last week I attended  an interesting lecture by Ran Gilad Bachrach from MSR. Ran presented CryptoNets who was reported in ICML 2016. CryptoNets allows to score trained deep learning models on encrypted data. They use homomorphic encryption a well known mechanism which allows computing encrypted products and sums. So the main trick is to limit the neural net operations to include only sums and products. To overcome this problem CryptoNet is using the square function as the only non-linear operation supported (vs. sigmoids, ReLU etc.)

On the up side, CryptoNets reports 99% accuracy on MNIST data which is the toy example everyone is using for deep learning. On the downside, you can not train a network but just score on new test data. Scoring is quite slow - around 5 minutes, although you can batch up to a few thousands scoring operations together at the same batch. Due to increasing complexity of the represented numbers the technique is also limited to a certain number of network layers.

I believe that in the coming few years additional research effort will be invested for trying to tackle the training of neural networks on private data without revealing the data contents.

Anyone who is interested in reading about other primitives who may be used for performing similar computation is welcome to take a look at my paper: D. Bickson, D. Dolev, G. Bezman and B. Pinkas Secure Multi-party Peer-to-Peer Numerical Computation. Proceedings of the 8th IEEE Peer-to-Peer Computing (P2P'08), Sept. 2008, Aachen, Germany - where we use both homomorphic encryption but also Shamir Secret Sharing to compute a similar distributed computation (in terms of sums and products).

Thursday, December 15, 2016

Neural networks for graphs

I met at Thomas Kipf from University of Amsterdam at NIPS 2016 and he pointed out some interesting blog post he wrote regarding neural networks for graph analytics.

Friday, June 24, 2016

Thursday, June 23, 2016

4th Large Scale Recommender Systems workshop - deadline extended

We have extended the deadline of our Large Scale Recommender Systems workshop to June 30. This is the 4th year we are organizing this workshop as part of ACM Recsys 2016. Anyone with novel work in the area of applied recommender systems is welcome to submit a talk proposal.

Sunday, June 19, 2016

GraphLab Create healthcare use case

A nice blog post from Mark Pinches, our Manchester evangelist who is working with John Snow  Labs. It shows how to use GraphLab Create for slicing, dicing and aggregations of healthcare data.

Saturday, June 18, 2016

Novomatic - comparing spark, pandas and Dato

Interesting slides from Bogdan Pirvu head data science @ Novomatic (Austrian Gaming Industries). I met Bogdan at RecSys Vienna last fall and he got interested in GraphLab Create. In his talk Bogdan compares pandas, Spark and Graphlab Create. Guess who is the winner?

Thursday, June 16, 2016

Prof. Alex Smola moves to Amazon

Here is the note he wrote on his blog. Alex will be heading Amazon Cloud ML effort. Definitely a great win for Amazon!

If you like to hear Alex speaking about his recent research you should attend our Data Science Summit July 12-13 in SF. You are welcome to use discount code DSSfriend.