31–40 of 98 posts
Will this be useful for machine learning in the same way as this ? https://medium.com/numerai/encrypted-data-for-efficient-mark...
This is very interesting from an academic/theory point of view. There currently aren't a lot practical use cases where we can afford a performance loss of ~100,000,000x (your homomorphic crypto algorithm is going to run on the order of ~Hz on a ~Ghz CPU).
Earlier quoted context omitted.
Homomorphic programs are designed to run without knowledge of the key. You have to process every piece of data in basically the same way. So I'm not entirely sure where you think the side-channel attack would arise.
because the simple fact you can process the data and examine the output reveals untold quantities of information about the key. the known plaintext attack breaks pretty much every crypto system, mix that with statistical analysis of this "processing" and I'm sure whatever is in the cloud will surrender its secrets pretty quick. And all that risk for what benefit? none of this processing will ever be faster than doing…
This isn't true at all.
Will this be useful for machine learning in the same way as this ? https://medium.com/numerai/encrypted-data-for-efficient-mark...
Yes, but not to the same degree. Numerai uses structure-preserving encryption / neural encryption. This allows people to use any existing machine learning algorithm on the data. For fully homomorphic encryption you would need specialized algorithms. These are way more difficult to design. They also run slower.
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...
That's a seriously cool thing to have in the toolbox! Does it produce only encrypted output, or can it optionally produce unencrypted results also? Can it optionally use public data as an input? Also I am guessing if it could be accelerated on GPUs. I worked with a guy who accelerated a standard FFT on CUDA 100..1000 times for scientific computations (and later NVidia copied his code, lol). I wonder if something simi…
The point is to be able to give encrypted data to a third party and have them do operations on that data (ex. sum all the values) and give you an encrypted result back. tldr. computations in the cloud with encrypted, private data
a) smart contracts controlling something within the encrypted data based on publicly available data;
b) encrypted key-value store where you traverse the tree structure based on encrypted query and encrypted tree buckets, but get a publicly available result about which bucket is next (similarly to how it was done in Arx paper using garbled circuits).
That's a seriously cool thing to have in the toolbox! Does it produce only encrypted output, or can it optionally produce unencrypted results also? Can it optionally use public data as an input? Also I am guessing if it could be accelerated on GPUs. I worked with a guy who accelerated a standard FFT on CUDA 100..1000 times for scientific computations (and later NVidia copied his code, lol). I wonder if something simi…
https://www.microsoft.com/en-us/research/wp-content/uploads/...
> We demonstrate CryptoNets on the MNIST optical character recognition tasks. CryptoNets achieve 99% accuracy and can make more than 51000 predictions per hour on a single PC. Therefore, they allow high throughput, accurate, and private predictions.
Will this be useful for machine learning in the same way as this ? https://medium.com/numerai/encrypted-data-for-efficient-mark...
Yes, but not to the same degree. Numerai uses structure-preserving encryption / neural encryption. This allows people to use any existing machine learning algorithm on the data. For fully homomorphic encryption you would need specialized algorithms. These are way more difficult to design. They also run slower.
This looks very interesting! However, not being an expert on FHE, is there a way to leverage this on current RDBMS systems for example? It says the library can evaluate binary gates. If we would like to run a SQL query for example, how do we translate it to a series of gates? Is it possible? Or is this so low level that we basically would need to build our own "processor" with binary gates and then build the rest of…
Although slower, it feels like TFHE would provide better security against an active adversary. So, at least, simple server-side encrypted queries would well be possible
Earlier quoted context omitted.
Yes, but not to the same degree. Numerai uses structure-preserving encryption / neural encryption. This allows people to use any existing machine learning algorithm on the data. For fully homomorphic encryption you would need specialized algorithms. These are way more difficult to design. They also run slower.
Worth pointing out that Numerai actually doesn't use encryption in any standard sense (including structure-preserving encryption), but instead seems to be using some heuristic method of obfuscating their data. 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…
It wouldn't be a simple hash or something, would it ?
One interesting thing about this: You are performing known operations on unknown data. But theoretically you could simulate a generic computer whose program is encrypted data as well, thus enabling unknown operations on unknown data. However, with speeds in the ms per gate we are a long way from that being practical right now.