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

Abstract

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-the-art 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.

Implementation of an Image Processing Algorithm on a Platform DSP Tms320c6416

“…Matrix-based encryption is much faster than existing asymmetric biometric identification schemes based on encryption. The security property of this system, however, has been reviewed and found to be unsafe [24]. Therefore, the authors of [24] have proposed a security-based matrix-based biometric identification scheme called CloudBI [24].…”

“…The security property of this system, however, has been reviewed and found to be unsafe [24]. Therefore, the authors of [24] have proposed a security-based matrix-based biometric identification scheme called CloudBI [24]. First, they show that the database and identification requests are publicly disclosed because of the lack of randomness in encryption.…”

Security and Privacy Enhancement for Outsourced Biometric Identification

“…However, such a scheme is not secure under an active attack where the cloud is able to collude with query users. Most recently, the authors in [6] proposed a secure outsourcing scheme that can defend against three different attacks as defined in [6], which include the attack where cloud and users are allowed to collude. The basic idea is to model the identification process as a Nearest Neighbor (NN)search problem.…”

“…Also, we point out that modelling the identification process as kNN search problem has inherent limitations in terms of template privacy. To be more specific, in [6], given two encrypted templates x and y and an encrypted query template z, the cloud can determine which template (x or y) is closer to z. Repeating this process, the cloud is able to identify the closet template to z.…”

SecHOG

“…Along another line, researches on privacypreserving computation on multimedia data begin to attract more and more attention in recent years. For example, [9] investigated face recognition on encrypted images, and [5,27] explored how to securely outsource computation of fingerprint identification to the cloud with privacy preservation.…”