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The second problem was more of an obstacle. Encrypting
and decrypting voice samples is straightforward—if you
have plenty of time to burn.
“When you encrypt data, the size of the data increases,”
says Rane. “With the thousand-bit keys typically considered
suitable for encrypted-domain processing, the overhead
for storage, communication and computation increases a
thousand-fold.”
Unlike text passwords, voice samples are complex, and
encrypting them generates huge amounts of data. Just
moving it back and forth from machine to machine
becomes time-prohibitive. In one experiment, Raj, then-
CMU graduate student Manas Pathak and colleagues
from Portugal’s Instituto de Engenharia de Sistemas e
Computadores Investigação e Desenvolvimento tried
encrypting, exchanging and decoding a 4.4-second voice
recording, using a 1024-bit security key. It took more
than 14 hours.
That wasn’t going to work—so how could they get around
the limitations of encrypted data
taking a trick from text passwords
The answer, ironically, was to make voiceprints behave
more like text-only passwords.
Most laypeople don’t realize that when they type in a
password, their computer does not transmit the password
to the system. Instead, it uses the password to generate a
“hash”—essentially, a mathematically devised “password
for your password” that only one particular system
possesses and uses. It’s almost impossible for someone
intercepting the hashed password to decode it. Hashes are
as secure as encrypted data, but far smaller, and easy to
transmit quickly.
Raj and his colleagues developed “secure binary
embeddings,” or SBEs, to convert key features of complex
voice signals into simplified hashes. The hashes of a
given recording can then be compared with the original
voiceprint’s hashes in a way that maintains recall and
precision without any way for the hashes to be used to
reconstruct the original voiceprints.
In a paper written with Jose Portelo of INESC-ID/IST,
Raj and colleagues showed that an SBE system could
deliver recall and precision in voice authentication of
over 93 percent—slightly under that of encryption-based
authentication. Since then, they have improved this
performance further.
“You can actually achieve more or less the same
performance that you get with a conventional
(encrypted) system, with a fraction of a percent error,”
Raj says. They’ve already achieved recall and precision
with less than 0.18 percent error—a level that suggests
SBE is a workable identification system for comparing
voiceprint information through the cloud.
Rane, who was not involved in the SBE research, says the
results Raj and his colleagues are reporting are “much
faster” than encryption. “You take a small hit in accuracy
in return for a very large increase in speed,” he says.
A few questions need to be resolved before SBE can be
deployed, Raj says. Importantly, they need to decide
how much of the conversion is performed on the user’s
device and how much is done in the system. The more of
the conversion done in the end-user’s device, the easier
it would be for an intruder to use that device to simply
reset everything for his or her purposes—for example, if
you lost a smart phone. On the other hand, the more that
resides in the system, the more the user is at the mercy of
that system’s security and goodwill.
Possibly, different levels of voice authentication would be
best served by different formulations of conversion.
Once these issues are decided, Raj says, voiceauthentication
passwords are “computationally feasible
and plausible, and can be implemented today.”
—Ken Chiacchia is a freelance writer of both science
fiction and science fact. He recently joined the Pittsburgh
Supercomputing Center as senior science writer.
the link.
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