1,732 research outputs found
Biometric Authentication System on Mobile Personal Devices
We propose a secure, robust, and low-cost biometric authentication system on the mobile personal device for the personal network. The system consists of the following five key modules: 1) face detection; 2) face registration; 3) illumination normalization; 4) face verification; and 5) information fusion. For the complicated face authentication task on the devices with limited resources, the emphasis is largely on the reliability and applicability of the system. Both theoretical and practical considerations are taken. The final system is able to achieve an equal error rate of 2% under challenging testing protocols. The low hardware and software cost makes the system well adaptable to a large range of security applications
Anonymous subject identification and privacy information management in video surveillance
The widespread deployment of surveillance cameras has raised serious privacy concerns, and many privacy-enhancing schemes have been recently proposed to automatically redact images of selected individuals in the surveillance video for protection. Of equal importance are the privacy and efficiency of techniques to first, identify those individuals for privacy protection and second, provide access to original surveillance video contents for security analysis. In this paper, we propose an anonymous subject identification and privacy data management system to be used in privacy-aware video surveillance. The anonymous subject identification system uses iris patterns to identify individuals for privacy protection. Anonymity of the iris-matching process is guaranteed through the use of a garbled-circuit (GC)-based iris matching protocol. A novel GC complexity reduction scheme is proposed by simplifying the iris masking process in the protocol. A user-centric privacy information management system is also proposed that allows subjects to anonymously access their privacy information via their iris patterns. The system is composed of two encrypted-domain protocols: The privacy information encryption protocol encrypts the original video records using the iris pattern acquired during the subject identification phase; the privacy information retrieval protocol allows the video records to be anonymously retrieved through a GC-based iris pattern matching process. Experimental results on a public iris biometric database demonstrate the validity of our framework
Privacy-Aware Processing of Biometric Templates by Means of Secure Two-Party Computation
The use of biometric data for person identification and access control is gaining more and more popularity. Handling biometric data, however, requires particular care, since biometric data is indissolubly tied to the identity of the owner hence raising important security and privacy issues. This chapter focuses on the latter, presenting an innovative approach that, by relying on tools borrowed from Secure Two Party Computation (STPC) theory, permits to process the biometric data in encrypted form, thus eliminating any risk that private biometric information is leaked during an identification process. The basic concepts behind STPC are reviewed together with the basic cryptographic primitives needed to achieve privacy-aware processing of biometric data in a STPC context. The two main approaches proposed so far, namely homomorphic encryption and garbled circuits, are discussed and the way such techniques can be used to develop a full biometric matching protocol described. Some general guidelines to be used in the design of a privacy-aware biometric system are given, so as to allow the reader to choose the most appropriate tools depending on the application at hand
SEMBA:SEcure multi-biometric authentication
Biometrics security is a dynamic research area spurred by the need to protect
personal traits from threats like theft, non-authorised distribution, reuse and
so on. A widely investigated solution to such threats consists in processing
the biometric signals under encryption, to avoid any leakage of information
towards non-authorised parties. In this paper, we propose to leverage on the
superior performance of multimodal biometric recognition to improve the
efficiency of a biometric-based authentication protocol operating on encrypted
data under the malicious security model. In the proposed protocol,
authentication relies on both facial and iris biometrics, whose representation
accuracy is specifically tailored to trade-off between recognition accuracy and
efficiency. From a cryptographic point of view, the protocol relies on SPDZ a
new multy-party computation tool designed by Damgaard et al. Experimental
results show that the multimodal protocol is faster than corresponding unimodal
protocols achieving the same accuracy
Incorporating Zero-Knowledge Succinct Non-interactive Argument of Knowledge for Blockchain-based Identity Management with off-chain computations
In today's world, secure and efficient biometric authentication is of keen
importance. Traditional authentication methods are no longer considered
reliable due to their susceptibility to cyber-attacks. Biometric
authentication, particularly fingerprint authentication, has emerged as a
promising alternative, but it raises concerns about the storage and use of
biometric data, as well as centralized storage, which could make it vulnerable
to cyber-attacks. In this paper, a novel blockchain-based fingerprint
authentication system is proposed that integrates zk-SNARKs, which are
zero-knowledge proofs that enable secure and efficient authentication without
revealing sensitive biometric information. A KNN-based approach on the FVC2002,
FVC2004 and FVC2006 datasets is used to generate a cancelable template for
secure, faster, and robust biometric registration and authentication which is
stored using the Interplanetary File System. The proposed approach provides an
average accuracy of 99.01%, 98.97% and 98.52% over the FVC2002, FVC2004 and
FVC2006 datasets respectively for fingerprint authentication. Incorporation of
zk-SNARK facilitates smaller proof size. Overall, the proposed method has the
potential to provide a secure and efficient solution for blockchain-based
identity management
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