Rosario Arjona

Bio: Rosario Arjona is an academic researcher from Spanish National Research Council. The author has contributed to research in topics: Fingerprint recognition & Fingerprint (computing). The author has an hindex of 5, co-authored 26 publications receiving 81 citations. Previous affiliations of Rosario Arjona include University of Seville.

Secure Combination of IoT and Blockchain by Physically Binding IoT Devices to Smart Non-Fungible Tokens Using PUFs.

Abstract: Non-fungible tokens (NFTs) are widely used in blockchain to represent unique and non-interchangeable assets. Current NFTs allow representing assets by a unique identifier, as a possession of an owner. The novelty introduced in this paper is the proposal of smart NFTs to represent IoT devices, which are physical smart assets. Hence, they are also identified as the utility of a user, they have a blockchain account (BCA) address to participate actively in the blockchain transactions, they can establish secure communication channels with owners and users, and they operate dynamically with several modes associated with their token states. A smart NFT is physically bound to its IoT device thanks to the use of a physical unclonable function (PUF) that allows recovering its private key and, then, its BCA address. The link between tokens and devices is difficult to break and can be traced during their lifetime, because devices execute a secure boot and carry out mutual authentication processes with new owners and users that could add new software. Hence, devices prove their trusted hardware and software. A whole demonstration of the proposal developed with ESP32-based IoT devices and Ethereum blockchain is presented, using the SRAM of the ESP32 microcontroller as the PUF.

Secure Management of IoT Devices Based on Blockchain Non-fungible Tokens and Physical Unclonable Functions.

Abstract: One of the most extended applications of blockchain technologies for the IoT ecosystem is the traceability of the data and operations generated and performed, respectively, by IoT devices. In this work, we propose a solution for secure management of IoT devices that participate in the blockchain with their own blockchain accounts (BCAs) so that the IoT devices themselves can sign transactions. Any blockchain participant (including IoT devices) can obtain and verify information not only about the actions or data they are taking but also about their manufacturers, managers (owners and approved), and users. Non Fungible Tokens (NFTs) based on the ERC-721 standard are proposed to manage IoT devices as unique and indivisible. The BCA of an IoT device, which is defined as an NFT attribute, is associated with the physical device since the secret seed from which the BCA is generated is not stored anywhere but a Physical Unclonable Function (PUF) inside the hardware of the device reconstructs it. The proposed solution is demonstrated and evaluated with a low-cost IoT device based on a Pycom Wipy 3.0 board, which uses the internal SRAM of the microcontroller ESP-32 as PUF. The operations it performs to reconstruct its BCA in Ethereum and to carry out transactions take a few tens of milliseconds. The smart contract programmed in Solidity and simulated in Remix requires low gas consumption.

Securing Minutia Cylinder Codes for Fingerprints through Physically Unclonable Functions: An Exploratory Study

Abstract: A number of personal devices, such as smartphones, have incorporated fingerprint recognition solutions for user authentication purposes. This work proposes a dual-factor fingerprint matching scheme based on P-MCCs (Protected Minutia Cylinder-Codes) generated from fingerprint images and PUFs (Physically Unclonable Functions) generated from device SRAMs (Static Random Access Memories). Combining the fingerprint identifier with the device identifier results in a secure template satisfying the discriminability, irreversibility, revocability, and unlinkability properties, which are strongly desired for data privacy and security. Experiments convey the benefits of the proposed dual-factor authentication mechanism in enhancing the security of personal devices that utilize biometric authentication schemes.

A hardware solution for real-time intelligent fingerprint acquisition

Abstract: The first step in any fingerprint recognition system is the fingerprint acquisition. A well-acquired fingerprint image results in high-resolution accuracy and low computational effort of processing. Hence, it is very useful for the recognition system to evaluate recognition confidence level to request new fingerprint samples if the confidence level is low, and to facilitate recognition process if the confidence level is high. This paper presents a hardware solution to ensure a successful and friendly acquisition of the fingerprint image, which can be incorporated at low cost into an embedded fingerprint recognition system due to its small size and high speed. The solution implements a novel technique based on directional image processing that allows not only the estimation of fingerprint image quality, but also the extraction of useful information (in particular, singular points). The digital architecture of the module is detailed and their features in terms of resource consumption and processing speed are illustrated with implementation results into FPGAs from Xilinx. Performance of the solution has been verified with fingerprints from several standard databases that have been acquired with sensors of different sizes and technologies (optical, capacitive, and thermal sweeping).

A PUF-and biometric-based lightweight hardware solution to increase security at sensor nodes

Abstract: Security is essential in sensor nodes which acquire and transmit sensitive data However, the constraints of processing, memory and power consumption are very high in these nodes Cryptographic algorithms based on symmetric key are very suitable for them The drawback is that secure storage of secret keys is required In this work, a low-cost solution is presented to obfuscate secret keys with Physically Unclonable Functions (PUFs), which exploit the hardware identity of the node In addition, a lightweight fingerprint recognition solution is proposed, which can be implemented in low-cost sensor nodes Since biometric data of individuals are sensitive, they are also obfuscated with PUFs Both solutions allow authenticating the origin of the sensed data with a proposed dual-factor authentication protocol One factor is the unique physical identity of the trusted sensor node that measures them The other factor is the physical presence of the legitimate individual in charge of authorizing their transmission Experimental results are included to prove how the proposed PUF-based solution can be implemented with the SRAMs of commercial Bluetooth Low Energy (BLE) chips which belong to the communication module of the sensor node Implementation results show how the proposed fingerprint recognition based on the novel texture-based feature named QFingerMap16 (QFM) can be implemented fully inside a low-cost sensor node Robustness, security and privacy issues at the proposed sensor nodes are discussed and analyzed with experimental results from PUFs and fingerprints taken from public and standard databases

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Handbook Of Biometrics

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Security and Accuracy of Fingerprint-Based Biometrics: A Review

Abstract: Biometric systems are increasingly replacing traditional password- and token-based authentication systems. Security and recognition accuracy are the two most important aspects to consider in designing a biometric system. In this paper, a comprehensive review is presented to shed light on the latest developments in the study of fingerprint-based biometrics covering these two aspects with a view to improving system security and recognition accuracy. Based on a thorough analysis and discussion, limitations of existing research work are outlined and suggestions for future work are provided. It is shown in the paper that researchers continue to face challenges in tackling the two most critical attacks to biometric systems, namely, attacks to the user interface and template databases. How to design proper countermeasures to thwart these attacks, thereby providing strong security and yet at the same time maintaining high recognition accuracy, is a hot research topic currently, as well as in the foreseeable future. Moreover, recognition accuracy under non-ideal conditions is more likely to be unsatisfactory and thus needs particular attention in biometric system design. Related challenges and current research trends are also outlined in this paper.

Cancelable Biometrics: a comprehensive survey

Abstract: Biometric recognition is a challenging research field but suffers from privacy and security concerns. To address this concern, Cancelable Biometrics is suggested in literature in which a Biometric image of a sample is distorted or transformed in such a manner that it becomes difficult to obtain the original Biometric image from the distorted one. Another important characteristic of Cancelable Biometrics is that it can be reissued if compromised. In this research paper, we present a comprehensive survey of more than 120 techniques suggested by various researchers from time to time for Cancelable Biometrics and a novel taxonomy for the same is developed. Further, various performance measures used in Cancelable Biometrics are reviewed and their mathematical formulations are given. Cancelable Biometrics also suffer from various security attacks as given in literature. A review of these security attacks is carried out. We have also performed a review of databases used in literature for nine different Cancelable Biometrics viz. Face, Iris, Speech, Fingerprint, Signature, Palmprint, ECG, Palmvein and Fingervein. Lastly, we have also given future research directions in this field. This study shall be useful for the researchers and practitioners working in this fascinating research area.

FPGA implementation of cost-effective robust Canny edge detection algorithm

Abstract: Implementation of Canny edge detection algorithm significantly outperforms the existing edge detection techniques in many computer vision algorithms. However, Canny edge detection algorithm is complex, time-consuming process with high hardware cost. To overcome these issues, a novel Canny edge detection algorithm is proposed in block level to detect edges without any loss. It uses sobel operator, approximation methods to compute gradient magnitude and orientation for replacing complex operations with reduced hardware cost, existing non-maximum suppression, block classification for adaptive thresholding and existing hysteresis thresholding. Pipelining is introduced to reduce latency. The proposed algorithm is implemented on Xilinx Virtex-5 FPGA and it provides better performance compared to frame-level Canny edge detection algorithm. The synthesized architecture reduces execution time by 6.8 % and utilizes less resource to detect edges of 512 × 512 image compared to existing distributed Canny edge detection algorithm.