scholarly journals Acceleration of Inner-Pairing Product Operation for Secure Biometric Verification

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2859
Author(s):  
Seong-Yun Jeon ◽  
Mun-Kyu Lee
Keyword(s):  
Mobile Technologies ◽  
Smart Devices ◽  
Biometric Data ◽  
Product Operation ◽  
Biometric Verification ◽  
Naive Method ◽  
Biometric Template ◽  
Need To Evaluate ◽  
Time Required ◽  
Pairing Product

With the recent advances in mobile technologies, biometric verification is being adopted in many smart devices as a means for authenticating their owners. As biometric data leakage may cause stringent privacy issues, many proposals have been offered to guarantee the security of stored biometric data, i.e., biometric template. One of the most promising solutions is the use of a remote server that stores the template in an encrypted form and performs a biometric comparison on the ciphertext domain, using recently proposed functional encryption (FE) techniques. However, the drawback of this approach is that considerable computation is required for the inner-pairing product operation used for the decryption procedure of the underlying FE, which is performed in the authentication phase. In this paper, we propose an enhanced method to accelerate the inner-pairing product computation and apply it to expedite the decryption operation of FE and for faster remote biometric verification. The following two important observations are the basis for our improvement—one of the two arguments for the decryption operation does not frequently change over authentication sessions, and we only need to evaluate the product of multiple pairings, rather than individual pairings. From the results of our experiments, the proposed method reduces the time required to compute an inner-pairing product by 30.7%, compared to the previous best method. With this improvement, the time required for biometric verification is expected to decrease by up to 10.0%, compared to a naive method.

scholarly journals Towards Better Performance for Protected Iris Biometric System with Confidence Matrix

Symmetry ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 910
Author(s):  
Tong-Yuen Chai ◽  
Bok-Min Goi ◽  
Wun-She Yap
Keyword(s):  
Data Privacy ◽  
Recognition Performance ◽  
Generalized Solution ◽  
Performance Degradation ◽  
Public Confidence ◽  
Data Types ◽  
Privacy And Security ◽  
Biometric Data ◽  
Biometric Template ◽  
Template Protection

Biometric template protection (BTP) schemes are implemented to increase public confidence in biometric systems regarding data privacy and security in recent years. The introduction of BTP has naturally incurred loss of information for security, which leads to performance degradation at the matching stage. Although efforts are shown in the extended work of some iris BTP schemes to improve their recognition performance, there is still a lack of a generalized solution for this problem. In this paper, a trainable approach that requires no further modification on the protected iris biometric templates has been proposed. This approach consists of two strategies to generate a confidence matrix to reduce the performance degradation of iris BTP schemes. The proposed binary confidence matrix showed better performance in noisy iris data, whereas the probability confidence matrix showed better performance in iris databases with better image quality. In addition, our proposed scheme has also taken into consideration the potential effects in recognition performance, which are caused by the database-associated noise masks and the variation in biometric data types produced by different iris BTP schemes. The proposed scheme has reported remarkable improvement in our experiments with various publicly available iris research databases being tested.

scholarly journals An Efficient and Privacy-Preserving Biometric Identification Scheme Based on the FITing-Tree

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Xiaopeng Yang ◽  
Hui Zhu ◽  
Songnian Zhang ◽  
Rongxing Lu ◽  
Xuesong Gao
Keyword(s):  
Homomorphic Encryption ◽  
Security Analysis ◽  
Privacy Preserving ◽  
Biometric Identification ◽  
Large Dataset ◽  
Biometric Data ◽  
Communication Costs ◽  
Identification Scheme ◽  
Biometric Template ◽  
Cloud Servers

Biometric identification services have been applied to almost all aspects of life. However, how to securely and efficiently identify an individual in a huge biometric dataset is still very challenging. For one thing, biometric data is very sensitive and should be kept secure during the process of biometric identification. On the other hand, searching a biometric template in a large dataset can be very time-consuming, especially when some privacy-preserving measures are adopted. To address this problem, we propose an efficient and privacy-preserving biometric identification scheme based on the FITing-tree, iDistance, and a symmetric homomorphic encryption (SHE) scheme with two cloud servers. With our proposed scheme, the privacy of the user’s identification request and service provider’s dataset is guaranteed, while the computational costs of the cloud servers in searching the biometric dataset can be kept at an acceptable level. Detailed security analysis shows that the privacy of both the biometric dataset and biometric identification request is well protected during the identification service. In addition, we implement our proposed scheme and compare it to a previously reported M-Tree based privacy-preserving identification scheme in terms of computational and communication costs. Experimental results demonstrate that our proposed scheme is indeed efficient in terms of computational and communication costs while identifying a biometric template in a large dataset.

scholarly journals RESEARCH TRENDS IN SOFTWARE ENGINEERING FIELD: A LITERATURE REVIEW

2020 ◽  
Vol 7 (6) ◽  
pp. 58-65
Author(s):  
Hiba Al Sghaier
Keyword(s):  
Software Engineering ◽  
Programming Languages ◽  
New Technologies ◽  
Research Trends ◽  
Mobile Technologies ◽  
Smart Devices ◽  
Software Systems ◽  
Current Time ◽  
Develop Software ◽  
Software Engineers

Software engineering is one of computer science branches, it comprises of building and developing software systems and applications. Software engineering is a discipline that has a constant growth in research in aim to identify new technologies and adopt it in different areas; there is a considerable investment on software engineering trends at the current time due to the availability of mobile technologies. With millions of billions of smart devices that are connected to the internet, all industries around the world are rapidly becoming a technology driven industries. Software engineers are aware of programming languages that are employed to develop software systems, by applying engineering principles to development process; they can design customized software systems for individual or organizational customers. The new trends in software engineering are numerous, Cloud Computing, machine learning, deep learning, big Data, mobile Computing. Nevertheless, there are many more other research trends in software engineering's field that have been intensively explored and implemented in many different industries. In this paper, authors try to summarize the most fields that are integrated with software engineering recently.

scholarly journals Utilizing Transfer Learning and Homomorphic Encryption in a Privacy Preserving and Secure Biometric Recognition System

Computers ◽  
2018 ◽  
Vol 8 (1) ◽  
pp. 3 ◽  
Author(s):  
Milad Salem ◽  
Shayan Taheri ◽  
Jiann-Shiun Yuan
Keyword(s):  
Transfer Learning ◽  
Search Algorithm ◽  
Homomorphic Encryption ◽  
Recognition System ◽  
Privacy Preserving ◽  
Modern World ◽  
Biometric Data ◽  
Liveness Detection ◽  
Biometric Verification ◽  
Feature Extractor

Biometric verification systems have become prevalent in the modern world with the wide usage of smartphones. These systems heavily rely on storing the sensitive biometric data on the cloud. Due to the fact that biometric data like fingerprint and iris cannot be changed, storing them on the cloud creates vulnerability and can potentially have catastrophic consequences if these data are leaked. In the recent years, in order to preserve the privacy of the users, homomorphic encryption has been used to enable computation on the encrypted data and to eliminate the need for decryption. This work presents DeepZeroID: a privacy-preserving cloud-based and multiple-party biometric verification system that uses homomorphic encryption. Via transfer learning, training on sensitive biometric data is eliminated and one pre-trained deep neural network is used as feature extractor. By developing an exhaustive search algorithm, this feature extractor is applied on the tasks of biometric verification and liveness detection. By eliminating the need for training on and decrypting the sensitive biometric data, this system preserves privacy, requires zero knowledge of the sensitive data distribution, and is highly scalable. Our experimental results show that DeepZeroID can deliver 95.47% F1 score in the verification of combined iris and fingerprint feature vectors with zero true positives and with a 100% accuracy in liveness detection.

scholarly journals Mobile Phones Help Develop Listening Skills

Informatics ◽  
2018 ◽  
Vol 5 (3) ◽  
pp. 32 ◽  
Author(s):  
Galina Artyushina ◽  
Olga Sheypak
Keyword(s):  
Mobile Phones ◽  
Computer Games ◽  
Language Teachers ◽  
Mobile Technologies ◽  
Smart Devices ◽  
English Learning ◽  
Listening Skills ◽  
Good Opportunity ◽  
Mobile Phone Use ◽  
Communicative Practice

Listening is one of the most difficult language skills among the four communication competences; however, it has received much less time in English learning than the other three (reading, writing, and speaking). Also, listening is often claimed to be a passive skill in the classroom, as learners seem to sit quietly and listen to dialogues. As language teachers, we are constantly striving to create the conditions under which our students can learn and succeed. At the same time, we meet challenges that may be detrimental to the learning process. This certainly applies to mobile phone use on the part of our students. It is a well-known fact that practically every student has at least one mobile device, as it has become a very convenient tool to get information. Unfortunately, students still prefer to use smart devices as entertainment, either to listen to music, watch films, or play computer games; it seems they really do not know how to use them in the process of education. This paper presents a review of how to get over difficulties in listening, and develop listening skills with the help of mobile phones outside the classroom. We have realized that to study English using mobile phones can consolidate our students’ understanding of what is being presented, or further contextualize the language to improve their ability to use it in communicative practice. To study English supposes this process to be non-durable, i.e., not only in the classroom under the guidance of the teacher. So, to study with the help of mobile technologies and handheld gadgets is a good opportunity to improve the quality and effectiveness of English learning.

Principles of App Selection and Training After Brain Injury

2014 ◽  
Vol 23 (3) ◽  
pp. 140-147 ◽  
Author(s):  
Michelle R. Wild
Keyword(s):  
Brain Injury ◽  
Smart Devices ◽  
Learning Tools ◽  
Clinical Environment ◽  
Daily Lives ◽  
Therapeutic Environment ◽  
Instructional Needs ◽  
Time Required ◽  
And Training ◽  
Sheer Number

The growing popularity of using mainstream smart devices as assistive technology for cognition (ATC) is having a significant impact in the daily lives of individuals living with brain injury. With more than 60 percent of the mobile market using smart devices, it is becoming more common for individuals to have their own smart devices. However, the devices are often underutilized and are not being used in a way conducive to benefiting individuals postinjury. Although brain injury professionals play a significant role in the selection and training of devices and apps, the sheer number of apps and the time required to select and train others to use them present major obstacles to the broad adoption of these devices in the therapeutic environment. The purpose of this article is to provide a framework for selecting and training the use of apps that helps clients with cognitive impairments function more optimally in their day-to-day lives. We present 4 questions to help identify training and instructional needs of clients. In addition, we discuss training templates and learning tools that can be used by therapists to facilitate app training within clinical sessions as well as by clients and/or caregivers outside the clinical environment.

scholarly journals A Comparison of Cloud Execution Mechanisms Fog, Edge, and Clone Cloud Computing

2018 ◽  
Vol 8 (6) ◽  
pp. 4646
Author(s):  
T. Francis
Keyword(s):  
Cloud Computing ◽  
Comparative Study ◽  
Mobile Devices ◽  
Mobile Cloud Computing ◽  
Fog Computing ◽  
Mobile Technologies ◽  
Smart Devices ◽  
Mobile Cloud ◽  
Attractive Feature ◽  
Cloud Technologies

Cloud computing is a technology that was developed a decade ago to provide uninterrupted, scalable services to users and organizations. Cloud computing has also become an attractive feature for mobile users due to the limited features of mobile devices. The combination of cloud technologies with mobile technologies resulted in a new area of computing called mobile cloud computing. This combined technology is used to augment the resources existing in Smart devices. In recent times, Fog computing, Edge computing, and Clone Cloud computing techniques have become the latest trends after mobile cloud computing, which have all been developed to address the limitations in cloud computing. This paper reviews these recent technologies in detail and provides a comparative study of them. It also addresses the differences in these technologies and how each of them is effective for organizations and developers.

scholarly journals RESEARCH TRENDS IN SOFTWARE ENGINEERING FIELD: A LITERATURE REVIEW

2020 ◽  
Vol 7 (6) ◽  
pp. 58-65
Author(s):  
Hiba Al Sghaier
Keyword(s):  
Software Engineering ◽  
Programming Languages ◽  
New Technologies ◽  
Research Trends ◽  
Mobile Technologies ◽  
Smart Devices ◽  
Software Systems ◽  
Current Time ◽  
Develop Software ◽  
Software Engineers

Software engineering is one of computer science branches, it comprises of building and developing software systems and applications. Software engineering is a discipline that has a constant growth in research in aim to identify new technologies and adopt it in different areas; there is a considerable investment on software engineering trends at the current time due to the availability of mobile technologies. With millions of billions of smart devices that are connected to the internet, all industries around the world are rapidly becoming a technology driven industries. Software engineers are aware of programming languages that are employed to develop software systems, by applying engineering principles to development process; they can design customized software systems for individual or organizational customers. The new trends in software engineering are numerous, Cloud Computing, machine learning, deep learning, big Data, mobile Computing. Nevertheless, there are many more other research trends in software engineering's field that have been intensively explored and implemented in many different industries. In this paper, authors try to summarize the most fields that are integrated with software engineering recently.

scholarly journals A Machine Learning approach for personal thermal comfort perception evaluation: experimental campaign under real and virtual scenarios

2020 ◽  
Vol 197 ◽  
pp. 04001
Author(s):  
Francesco Salamone ◽  
Alice Bellazzi ◽  
Lorenzo Belussi ◽  
Gianfranco Damato ◽  
Ludovico Danza ◽  
...  
Keyword(s):  
Machine Learning ◽  
Thermal Comfort ◽  
Environmental Parameters ◽  
Smart Devices ◽  
Thermal Perception ◽  
Biometric Data ◽  
Experimental Campaign ◽  
Machine Learning Approach ◽  
Comfort Perception ◽  
External Stimuli

Personal Thermal Comfort models differ from the steady-state methods because they consider personal user feedback as target value. Today, the availability of integrated “smart” devices following the concept of the Internet of Things and Machine Learning (ML) techniques allows developing frameworks reaching optimized indoor thermal comfort conditions. The article investigates the potential of such approach through an experimental campaign in a test cell, involving 25 participants in a Real (R) and Virtual (VR) scenario, aiming at evaluating the effect of external stimuli on personal thermal perception, such as the variation of colours and images of the environment. A dataset with environmental parameters, biometric data and the perceived comfort feedbacks of the participants is defined and managed with ML algorithms in order to identify the most suitable one and the most influential variables that can be used to predict the Personal Thermal Comfort Perception (PTCP). The results identify the Extra Trees classifier as the best algorithm. In both R and VR scenario a different group of variables allows predicting PTCP with high accuracy.

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