![](bg6.png)
more robustness and better true positive rates.
For the identification of telephone spam, a signif-
icant rate of false negatives can be accepted since the
audio data will be replayed a number of times. But
false positive identifications of telephone spam should
be avoided, even for large hash repositories.
5 CONCLUSIONS
We studied the security and privacy requirements
of audio fingerprints and analyzed the existing ap-
proaches and algorithms. There exist various pow-
erful fingerprinting frameworks which permit an effi-
cient identification of audio samples. Some work has
been done on the security of audio hashes, but open
issues remain if the hash is used for multimedia au-
thentication and watermarking. This contribution an-
alyzes the privacyissues which are relevant for speech
data, for example to identify replayed telephone data
(spam calls). The fingerprint should not leak informa-
tion on the original audio data.
By modifying well known audio fingerprinting al-
gorithms and combining them with a cryptographic
message authentication code, we defined a random-
ized audio hash which consists of a set of binary vec-
tors. We estimated the entropy of the subhash values
which is important for the security properties of the
proposed method. Furthermore, we analyzed the per-
formance in terms of robustness and discrimination
power. We showed that the hash has adequate robust-
ness, at least if the audio samples have sufficient audio
quality, and excellent discrimination capabilities. The
hash permits an efficient identification of speech sig-
nals in large databases and prevents the exposure of
audio content.
Future work will incorporate additional audio ma-
terial and extend the study of the security properties
of robust keyed hash functions.
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