CloudBI: Practical Privacy-Preserving Outsourcing of Biometric Identification in the Cloud

Biometric identification has been incredibly useful in the law enforcement to authenticate an individual’s identity and/or to figure out who someone is, typically by scanning a database of records for a close enough match. In this work, we investigate the privacy-preserving biometric identification outsourcing problem, where the database owner outsources both the large-scale encrypted database and the computationally intensive identification job to the semi-honest cloud, relieving itself from data storage and computation burden. We present new privacypreserving biometric identification protocols, which substantially reduce the computation burden on the database owner. Our protocols build on new biometric data encryption, distance-computation and matching algorithms that novelly exploit inherent structures of biometric data and properties of identification operations. A thorough security analysis shows that our solutions are practically-secure, and the ultimate solution offers a higher level of privacy protection than the-state-of-theart on biometric identification outsourcing. We evaluate our protocols by implementing an efficient privacy-preserving fingerprint-identification system, showing that our protocols meet both the security and efficiency needs well, and they are appropriate for use in various privacy-preserving biometric identification applications.