Microsoft boosts the speed of homomorphic encryption systems
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February 9, 2016
In partnership with academia, Microsoft researchers have published a new paper detailing how they
have dramatically increased the speed of homomorphic encryption systems.
Homomorphic encryption is a form of data encryption technology that allows computations to be carried
out on ciphertext, thus generating an encrypted result which, when decrypted, matches the result of
operations performed on the plaintext.
It is sometimes a desirable feature in modern communication system architectures. Homomorphic encryption
would allow the chaining together of different services without exposing the data to each of those services.
With a standard encryption system, data is scrambled and then decrypted when it needs to be processed,
leaving it vulnerable to data theft.
Homomorphic encryption, first proposed in 1978 but only really refined in the last decade thanks
to greatly increased computing power, allows software to analyze and modify encrypted data without
decrypting it into plaintext first.
The data stays encrypted while operations are performed on it-– provided you have the correct key, of course.
This has major overall advantages from a security standpoint. For example, hospital records can be examined
without compromising patient privacy, financial data can be analyzed without opening it up to theft, and it's perfect
for a computing environment where so much data is cloud-based on someone else's servers.
But nothing is perfect, and there are limitations. The first fully working homomorphic encryption
system, built by Craig Gentry (now an IBM Research cryptographer), was incredibly slow, taking 100 trillion
times as long to perform calculations of encrypted data than plaintext analysis.
IBM has sped things up considerably, making calculations on a 16-core server over two million times
faster than past systems, and has open-sourced part of the technology. But in a new paper, Microsoft thinks
it has made a huge leap forward in applying the encryption system to deep learning neural networks.
Professor Kristin Lauter, principle research manager at Microsoft, told us that the team has developed
CryptoNets that process the encrypted information. The team claims that its optical recognition system is
capable of making 51,000 predictions per hour with 99 percent accuracy.
The key to Microsoft's approach is in the pre-processing work. The researchers need to know in advance
the complexity of the arithmetic circuit that is to be applied to the data.
They need to structure the neural network appropriately and keep data loads small enough so the computer
handling them isn't over-worked.
To make this possible, the team developed the Simple Encrypted Arithmetic Library (SEAL) – code which it
revealed last November. Detailed parameters have to be set up before the data run is attempted, to keep
multiplication levels low, however.
In various test runs, the team used 28 x 28-pixel images of handwritten words taken from the Mixed
National Institute of Standards and Technology (MNIST) database and ran 50,000 samples through to
'train' the new system.
They then tried a full run on an additional 10,000 characters to test its accuracy. The test rig
was a PC with a single Intel Xeon E5-1620 CPU running at 3.5 GHz, with 16 GB of RAM, running Windows 10.
They then structured the data in parallel, and the computer ran 51,739 predictions per hour with an
accuracy rate of 99 percent.
But as can be expected, there's still a lot of work to be done, Lauter said, but the initial results
look promising and could be used for a kind of machine learning-as-a-service concept, or on specialist
devices for medical or Wall Street financial predictions.
"I'm not in that part of the company's decision-making process, so I can't guarantee when Microsoft will
have a product using this technology," said Lauter. "But from a research point of view, we are definitely going
towards making it available to customers and the community."
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