  Fingerprint authentication is a popular security mechanism for smartphones
and laptops. However, its adoption in web and cloud environments has been
limited due to privacy concerns over storing and processing biometric data on
servers. This paper introduces Blind-Touch, a novel machine learning-based
fingerprint authentication system leveraging homomorphic encryption to address
these privacy concerns. Homomorphic encryption allows computations on encrypted
data without decrypting. Thus, Blind-Touch can keep fingerprint data encrypted
on the server while performing machine learning operations. Blind-Touch
combines three strategies to efficiently utilize homomorphic encryption in
machine learning: (1) It optimizes the feature vector for a distributed
architecture, processing the first fully connected layer (FC-16) in plaintext
on the client side and the subsequent layer (FC-1) post-encryption on the
server, thereby minimizing encrypted computations; (2) It employs a homomorphic
encryption compatible data compression technique capable of handling 8,192
authentication results concurrently; and (3) It utilizes a clustered server
architecture to simultaneously process authentication results, thereby
enhancing scalability with increasing user numbers. Blind-Touch achieves high
accuracy on two benchmark fingerprint datasets, with a 93.6% F1- score for the
PolyU dataset and a 98.2% F1-score for the SOKOTO dataset. Moreover,
Blind-Touch can match a fingerprint among 5,000 in about 0.65 seconds. With its
privacy focused design, high accuracy, and efficiency, Blind-Touch is a
promising alternative to conventional fingerprint authentication for web and
cloud applications.
