An Efficient Multi-Modal Biometric Sensing and Authentication Framework for Distributed Applications

Tarannum, Ayesha; Rahman, Zia Ur; Rao, L. Koteswara; Tadisetty, Srinivasulu; Lay-Ekuakille, A.

doi:10.1109/jsen.2020.3012536

Cited by 26 publications

(7 citation statements)

References 22 publications

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“…An attacker can gain direct access to private data if they are able to correctly guess the access pattern. There is a possibility that cloud attackers, in addition to other types of attackers, could catch the exposed path of access and exploit it [59]. Internet criminals are always developing new methods to gain unauthorized access to information and use it to their commercial benefit.…”

Section: Working Of Strong Fedorammentioning

confidence: 99%

Big Data Privacy and Security Using Abundant Data Recovery Techniques and Data Obliviousness Methodologies

Funde

Swain

2022

IEEE Access

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The concept of big data security is introduced in this article along with many features. It illustrates the need for security in healthcare systems as the volume of data increases continuously over the period of time. The necessity of big data security as well as several big data analytics phases highlighted. It covers many big data privacy-preserving strategies. Many digital storage solutions being used in today's world are designed to work only with fixed format of the data. This paper introduces some methods for maintaining metadata obliviousness. The oblivious RAM technology mentioned in the research article address security concerns and it can be handled with the daily increase in data in several industries. Security needs are introduced at many phases of big data creation, such as information extraction, storage systems, and analytics of the information. Additionally, it presents several data recovery methods for recovering original data in the event of a data crash. This paper covers several data categorization methods for sorting data into normal and sensitive categories as well as methods for anomaly detection. It discusses the advantages and disadvantages of various security measures.INDEX TERMS Security, privacy, obliviousness, data recovery. I. INTRODUCTIONThe core concepts and phases of big data are introduced in this section. At several stages of big data production, such as data generation, data storage, and data analytics, security requirements are introduced. The importance of security in big data, cloud, and Internet of Things (IoT) infrastructure is discussed in this section. It discusses about several security measures that must be defended against various kinds of attacks. It discusses on the security of healthcare systems. It gives a general overview of the medical industry and discusses how patient data must be safeguarded if it is stored in a dispersed setting. A. BIG DATABig data is data that has large volume, heterogeneity, speed, and volatility all at the same time. The term "high volume" denotes to the significant quantity of data that is produced on a daily basis by a variety of companies and organizations. The rapid proliferation of new data types, many of which are going to be incorporated in current datasets that have been acquired from a variety of companies and devices, is referred to as heterogeneity. The term "high speed" refers to the rapid rate at which data is collected or captured from a variety of social networking sites into a database [1]. Huge amounts of data present additional difficulties to the security systems that are already in place due to the variety, volume, and unpredictability of the data. Most data storage models used today are designed to work with data that has been properly arranged. The currently available encryption methods are inefficient for use with massive data as once data has been encrypted, producing keys, encrypting it, and then decrypting it takes a significant amount of time. For instance, the data collected from patients, various sensors, soc...

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Section: Working Of Strong Fedorammentioning

confidence: 99%

Big Data Privacy and Security Using Abundant Data Recovery Techniques and Data Obliviousness Methodologies

Funde

Swain

2022

IEEE Access

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“…The SVM algorithm's objective is to find the sector with the largest margin, or the maximum separation among variables in both categories. Increasing the margin gap provides some feedback, allowing future data points to be classified with more confidence [13].…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

“…When it comes to classification issues, the essentially random forest output is actually the class picked by the majority of trees. Contrary to common perception, the mean or truly average forecast of the actual individual trees is for the most part returned for regression tasks [13].…”

Section: Random Forest (Rf)mentioning

confidence: 99%

A machine learning based sensing and measurement framework for timing of volcanic eruption and categorization of seismic data

2022

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The circumstances and factors which determine the volcanic explosive ejection are unknown, and currently, there is no effective way to determine the end of a volcanic explosive ejection. At present, the end of an eruption is determined by either generalized standards or the measurement which is unique to the volcano. We investigate the use of controlled machine learning techniques such as Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), and Gaussian Process Classifiers (GPC), and create a decisiveness index D to assess the uniformity of the groups provided by these machine learning models. We analyzed the measured end-date obtained by seismic information categorization is two to four months later than the end-dates determined by the earliest instance of visible eruption for both volcanic systems. Likewise, the measurement systems, measurement technology becomes key elements in the seismic data analysis. The findings are consistent across models and correspond to previous, broad definitions of ejection. Obtained classifications demonstrate a more significant relationship between eruptive movement and visual activity than information base records of ejection start and completion timings. Our research has presented a new measurement-based categorization technique for studying volcanic eruptions, which provides a reliable tool for determining whether or not an emission has stopped without the need for visual confirmation.

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“…In some attempts, the researchers use a single classifier to train on fingerprint images and classify the test images [12,4,13,14,15,16]. Some single classifiers outperform the ensemble-based classifiers, but mostly they are useful when the problem is targeted as a closed set problem.…”

Section: Related Workmentioning

confidence: 99%

EaZy Learning: An Adaptive Variant of Ensemble Learning for Fingerprint Liveness Detection

Agarwal¹,

Chowdary²,

Sourabh³

2021

Preprint

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In the field of biometrics, fingerprint recognition systems are vulnerable to presentation attacks made by artificially generated spoof fingerprints. Therefore, it is essential to perform liveness detection of a fingerprint before authenticating it. Fingerprint liveness detection mechanisms perform well under the within-dataset environment but fail miserably under cross-sensor (when tested on a fingerprint acquired by a new sensor) and cross-dataset (when trained on one dataset and tested on another) settings. To enhance the generalization abilities, robustness and the interoperability of the fingerprint spoof detectors, the learning models need to be adaptive towards the data. We propose a generic model, EaZy learning which can be considered as an adaptive midway between eager and lazy learning. We show the usefulness of this adaptivity under crosssensor and cross-dataset environments. EaZy learning examines the properties intrinsic to the dataset while generating a pool of hypotheses. EaZy learning is similar to ensemble learning as it generates an ensemble of base classifiers and integrates them to make a prediction. Still, it differs in the way it generates the base classifiers. EaZy learning develops an ensemble of entirely disjoint base classifiers which has a beneficial influence on the diversity of the underlying ensemble. Also, it integrates the predictions made by these base classifiers based on their performance on the validation data. Experiments conducted on the standard high dimensional datasets LivDet 2011, LivDet 2013 and LivDet 2015 prove the efficacy of the model under cross-dataset and cross-sensor environments.

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An Efficient Multi-Modal Biometric Sensing and Authentication Framework for Distributed Applications

Cited by 26 publications

References 22 publications

Big Data Privacy and Security Using Abundant Data Recovery Techniques and Data Obliviousness Methodologies

Big Data Privacy and Security Using Abundant Data Recovery Techniques and Data Obliviousness Methodologies

A machine learning based sensing and measurement framework for timing of volcanic eruption and categorization of seismic data

EaZy Learning: An Adaptive Variant of Ensemble Learning for Fingerprint Liveness Detection

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