  Mounting privacy legislation calls for the preservation of privacy in speech
technology, though solutions are gravely lacking. While evaluation campaigns
are long-proven tools to drive progress, the need to consider a privacy
adversary implies that traditional approaches to evaluation must be adapted to
the assessment of privacy and privacy preservation solutions. This paper
presents the first step in this direction: metrics.
  We introduce the zero evidence biometric recognition assessment (ZEBRA)
framework and propose two new privacy metrics. They measure the average level
of privacy preservation afforded by a given safeguard for a population and the
worst-case privacy disclosure for an individual. The paper demonstrates their
application to privacy preservation assessment within the scope of the
VoicePrivacy challenge. While the ZEBRA framework is designed with speech
applications in mind, it is a candidate for incorporation into biometric
information protection standards and is readily extendable to the study of
privacy in applications even beyond speech and biometrics.
