Google neural networksi: Learning to Keep Secrets...Invents it's own encryption
26
October 2016
Google’s
neural networks invent their own encryption
Machines have
been learning how to send secret messages to each other.
Computers are keeping secrets. A
team from Google Brain, Google’s deep learning project, has shown that machines
can learn how to protect their messages from prying eyes.
Researchers Martín Abadi and David Andersen demonstrate
that neural networks, or “neural nets” – computing systems
that are loosely based on artificial neurons – can work out how to use a simple
encryption technique.
In their experiment, computers
were able to make their own form of encryption using machine learning, without
being taught specific cryptographic algorithms. The encryption was very basic,
especially compared to our current human-designed systems. Even so, it is still
an interesting step for neural nets, which the authors state “are generally not
meant to be great at cryptography”.
The Google Brain team started
with three neural nets called Alice, Bob and Eve. Each system was trained to
perfect its own role in the communication. Alice’s job was to send a secret
message to Bob, Bob’s job was to decode the message that Alice sent, and Eve’s
job was to attempt to eavesdrop.
To make sure the message remained
secret, Alice had to convert her original plain-text message into complete
gobbledygook, so that anyone who intercepted it (like Eve) wouldn’t be able to
understand it. The gobbledygook – or “cipher text” – had to be decipherable by
Bob, but nobody else. Both Alice and Bob started with a pre-agreed set of
numbers called a key, which Eve didn’t have access to, to help encrypt and
decrypt the message.
Practice
makes perfect
Initially, the neural nets were
fairly poor at sending secret messages. But as they got more practice, Alice
slowly developed her own encryption strategy, and Bob worked out how to decrypt
it.
After the scenario had been played out 15,000 times, Bob was
able to convert Alice’s cipher text message back into plain text, while Eve
could guess just 8 of the 16 bits forming the message. As each bit was just a 1
or a 0, that is the same success rate you would expect from pure chance. The
research is published on arXiv.
We don’t know exactly how the
encryption method works, as machine learning provides a solution but not an
easy way to understand how it is reached. In practice, this also means that it
is hard to give any security guarantees for an encryption method created in
this way, so the practical implications for the technology could be limited.
“Computing with neural nets on
this scale has only become possible in the last few years, so we really are at
the beginning of what’s possible,” says Joe Sturonas of encryption company
PKWARE in Milwaukee, Wisconsin.
Computers have a very long way to
go if they’re to get anywhere near the sophistication of human-made encryption
methods. They are, however, only just starting to try.
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