Hardware Accelerator Approach Towards Efficient Biometric Cryptosystems for Network Security

Protecting data and its communication is a critical part of the modern network. The science of protecting data, known as cryptography, uses secret keys to encrypt data in a format that is not easily decipherable. However, most commonly secure logons for a workstation connected to a network use passwords to perform user authentication. These passwords are a weak link in the security chain, and are a common point of attack on cryptography schemes. One alternative to password usage for network security is to use a person’s physical characteristics to verify who the person is and unlock the data correspondingly. This study focuses on the Cambridge biometric cryptosystem, a system for performing user authentication based on a user’s iris data. The implementation of this system expanded from a single-core software-only system to a collaborative system consisting of a single core and a hardware accelerator. The experiment takes place on a Xilinx Zynq-7000 All Programmable SoC. Software implementation is performed on one of the embedded ARM A9 cores while hardware implementation makes use of the programmable logic. Our hardware acceleration produced a speedup of 2.2X while reducing energy usage to 47.5 % of its original value for the combined enrolment and verification process. These results are also compared to a many-core acceleration of the same system, providing an analysis of different acceleration methods

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

Roadmap on optical security

Postprint (author's final draft

Privacy-preserving comparison of variable-length data with application to biometric template protection

The establishment of cloud computing and big data in a wide variety of daily applications has raised some privacy concerns due to the sensitive nature of some of the processed data. This has promoted the need to develop data protection techniques, where the storage and all operations are carried out without disclosing any information. Following this trend, this paper presents a new approach to efficiently compare variable-length data in the encrypted domain using homomorphic encryption where only encrypted data is stored or exchanged. The new variable-length-based algorithm is fused with existing fixed-length techniques in order to obtain increased comparison accuracy. To assess the soundness of the proposed approach, we evaluate its performance on a particular application: a multi-algorithm biometric template protection system based on dynamic signatures that complies with the requirements described in the ISO/IEC 24745 standard on biometric information protection. Experiments have been carried out on a publicly available database and a free implementation of the Paillier cryptosystem to ensure reproducibility and comparability to other schemes.This work was supported in part by the German Federal Ministry of Education and Research (BMBF); in part by the Hessen State Ministry for Higher Education, Research, and the Arts (HMWK) within the Center for Research in Security and Privacy (CRISP); in part by the Spanish Ministerio de Economia y Competitividad / Fondo Europeo de Desarrollo Regional through the CogniMetrics Project under Grant TEC2015-70627-R; and in part by Cecaban

Securing Cloud Storage by Transparent Biometric Cryptography

With the capability of storing huge volumes of data over the Internet, cloud storage has become a popular and desirable service for individuals and enterprises. The security issues, nevertheless, have been the intense debate within the cloud community. Significant attacks can be taken place, the most common being guessing the (poor) passwords. Given weaknesses with verification credentials, malicious attacks have happened across a variety of well-known storage services (i.e. Dropbox and Google Drive) – resulting in loss the privacy and confidentiality of files. Whilst today's use of third-party cryptographic applications can independently encrypt data, it arguably places a significant burden upon the user in terms of manually ciphering/deciphering each file and administering numerous keys in addition to the login password. The field of biometric cryptography applies biometric modalities within cryptography to produce robust bio-crypto keys without having to remember them. There are, nonetheless, still specific flaws associated with the security of the established bio-crypto key and its usability. Users currently should present their biometric modalities intrusively each time a file needs to be encrypted/decrypted – thus leading to cumbersomeness and inconvenience while throughout usage. Transparent biometrics seeks to eliminate the explicit interaction for verification and thereby remove the user inconvenience. However, the application of transparent biometric within bio-cryptography can increase the variability of the biometric sample leading to further challenges on reproducing the bio-crypto key. An innovative bio-cryptographic approach is developed to non-intrusively encrypt/decrypt data by a bio-crypto key established from transparent biometrics on the fly without storing it somewhere using a backpropagation neural network. This approach seeks to handle the shortcomings of the password login, and concurrently removes the usability issues of the third-party cryptographic applications – thus enabling a more secure and usable user-oriented level of encryption to reinforce the security controls within cloud-based storage. The challenge represents the ability of the innovative bio-cryptographic approach to generate a reproducible bio-crypto key by selective transparent biometric modalities including fingerprint, face and keystrokes which are inherently noisier than their traditional counterparts. Accordingly, sets of experiments using functional and practical datasets reflecting a transparent and unconstrained sample collection are conducted to determine the reliability of creating a non-intrusive and repeatable bio-crypto key of a 256-bit length. With numerous samples being acquired in a non-intrusive fashion, the system would be spontaneously able to capture 6 samples within minute window of time. There is a possibility then to trade-off the false rejection against the false acceptance to tackle the high error, as long as the correct key can be generated via at least one successful sample. As such, the experiments demonstrate that a correct key can be generated to the genuine user once a minute and the average FAR was 0.9%, 0.06%, and 0.06% for fingerprint, face, and keystrokes respectively. For further reinforcing the effectiveness of the key generation approach, other sets of experiments are also implemented to determine what impact the multibiometric approach would have upon the performance at the feature phase versus the matching phase. Holistically, the multibiometric key generation approach demonstrates the superiority in generating the bio-crypto key of a 256-bit in comparison with the single biometric approach. In particular, the feature-level fusion outperforms the matching-level fusion at producing the valid correct key with limited illegitimacy attempts in compromising it – 0.02% FAR rate overall. Accordingly, the thesis proposes an innovative bio-cryptosystem architecture by which cloud-independent encryption is provided to protect the users' personal data in a more reliable and usable fashion using non-intrusive multimodal biometrics.Higher Committee of Education Development in Iraq (HCED

A Survey on Modality Characteristics, Performance Evaluation Metrics, and Security for Traditional and Wearable Biometric Systems

Biometric research is directed increasingly towards Wearable Biometric Systems (WBS) for user authentication and identification. However, prior to engaging in WBS research, how their operational dynamics and design considerations differ from those of Traditional Biometric Systems (TBS) must be understood. While the current literature is cognizant of those differences, there is no effective work that summarizes the factors where TBS and WBS differ, namely, their modality characteristics, performance, security and privacy. To bridge the gap, this paper accordingly reviews and compares the key characteristics of modalities, contrasts the metrics used to evaluate system performance, and highlights the divergence in critical vulnerabilities, attacks and defenses for TBS and WBS. It further discusses how these factors affect the design considerations for WBS, the open challenges and future directions of research in these areas. In doing so, the paper provides a big-picture overview of the important avenues of challenges and potential solutions that researchers entering the field should be aware of. Hence, this survey aims to be a starting point for researchers in comprehending the fundamental differences between TBS and WBS before understanding the core challenges associated with WBS and its design

Design and implementation of a multi-modal biometric system for company access control

This paper is about the design, implementation, and deployment of a multi-modal biometric system to grant access to a company structure and to internal zones in the company itself. Face and iris have been chosen as biometric traits. Face is feasible for non-intrusive checking with a minimum cooperation from the subject, while iris supports very accurate recognition procedure at a higher grade of invasivity. The recognition of the face trait is based on the Local Binary Patterns histograms, and the Daughman\u2019s method is implemented for the analysis of the iris data. The recognition process may require either the acquisition of the user\u2019s face only or the serial acquisition of both the user\u2019s face and iris, depending on the confidence level of the decision with respect to the set of security levels and requirements, stated in a formal way in the Service Level Agreement at a negotiation phase. The quality of the decision depends on the setting of proper different thresholds in the decision modules for the two biometric traits. Any time the quality of the decision is not good enough, the system activates proper rules, which ask for new acquisitions (and decisions), possibly with different threshold values, resulting in a system not with a fixed and predefined behaviour, but one which complies with the actual acquisition context. Rules are formalized as deduction rules and grouped together to represent \u201cresponse behaviors\u201d according to the previous analysis. Therefore, there are different possible working flows, since the actual response of the recognition process depends on the output of the decision making modules that compose the system. Finally, the deployment phase is described, together with the results from the testing, based on the AT&T Face Database and the UBIRIS database