A Multifaceted Role of Biometrics in Online Security, Privacy, and Trustworthy Decision Making

Gavrilova, Marina L.; Anzum, Fahim; Bari, A. S. M. Hossain; Bhatia, Yajurv; Iffath, Fariha; Ohi, Quwsar; Shopon,; Wahid, Zaman

doi:10.1007/978-3-031-10706-1_14

Cited by 11 publications

(10 citation statements)

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“…Emotion is essential in every aspect of a person's life, influencing decision-making, social relationships, and behavior. As a result, there has been a surge in research on the application of artificial intelligence (AI) and machine learning (ML) techniques to infer users' emotional states from their social media content [4], [5]. Automatic emotion detection (ED) involves using natural language processing (NLP) techniques and machine learning algorithms to decipher an individual's emotional states [6].…”

Section: Introductionmentioning

confidence: 99%

“…In text-based emotion detection, natural language processing (NLP) techniques are employed to extract meaningful patterns from the text data, that are leveraged by ML algorithms to infer the users' emotions. Text-based emotion detection has important applications in various fields, including customer service, mental health support, opinion mining, and personalization [4]. It enables machines to recognize and understand the emotions expressed in text, which can improve communication and has the potential to enhance user experience.…”

Section: Introductionmentioning

confidence: 99%

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Emotion Detection From Micro-Blogs Using Novel Input Representation

Anzum

Gavrilova

2023

IEEE Access

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Emotion is a natural intrinsic state of mind that drives human behavior, social interaction, and decisionmaking. Due to the rapid expansion in the current era of the Internet, online social media (OSM) platforms have become popular means of expressing opinions and communicating emotions. With the emergence of natural language processing (NLP) techniques powered by artificial intelligence (AI) algorithms, emotion detection (ED) from user-generated OSM data has become a prolific research domain. However, it is challenging to extract meaningful features for identifying discernible patterns from the short, informal, and unstructured texts that are common on micro-blogging platforms like Twitter. In this paper, we introduce a novel representation of features extracted from user-generated Twitter data that can capture users' emotional states. An advanced approach based on Genetic Algorithm (GA) is used to construct the input representation which is composed of stylistic, sentiment, and linguistic features extracted from tweets. A voting ensemble classifier with weights optimized by a GA is introduced to increase the accuracy of emotion detection using the novel feature representation. The experimental results demonstrate that the proposed approach outperforms the state-of-the-art classical machine learning-based emotion detection techniques, achieving the highest level of precision (96.49%), recall (96.49%), F1-score (96.49%), and accuracy (96.49%).

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Emotion Detection From Micro-Blogs Using Novel Input Representation

Anzum

Gavrilova

2023

IEEE Access

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“…Biometric credentials are becoming popular means of authenticating a person. 1,2 Among several emerging biometric-based approaches, fingerprint and face-based authentication mechanisms are the most widely used. 3,4 The features of biometric traits and their authentication mechanisms, such as universality, permanence, uniqueness, the low storage requirement for storing biometric templates, ease of use in fields, inexpensive scanning devices, and so forth, 5 make biometric authentication an attractive option for a variety of applications such as in banking, for home security, and so forth.…”

Section: Introductionmentioning

confidence: 99%

“…Biometric credentials are becoming popular means of authenticating a person 1,2 . Among several emerging biometric‐based approaches, fingerprint and face‐based authentication mechanisms are the most widely used 3,4 .…”

Section: Introductionmentioning

confidence: 99%

A protocol to establish trust on biometric authentication devices

Kiran

Moona

Biswas

2023

Security and Privacy

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One of the most extensively utilized mechanisms for person authentication is a system built using biometric‐based authentication. However, many applications use biometric authentication devices that do not support any device authentication mechanisms. As a result, a counterfeit scanning device may be substituted for the genuine one. Non‐authentic biometric authentication devices may perform some additive / subtractive or malicious functions. This paper proposes a technique for establishing trust in biometric authentication devices. The device authentication procedure is essential to build trust in biometric authentication devices such that non‐genuine biometric authentication devices are not used, which may compromise the loss of authentication factor and its replay when the genuine user is not getting authenticated. The protocol uses strong cryptographic mechanisms to authenticate the biometric authentication device with the application server and includes mechanisms for protection against the tampering of biometric templates and to prevent replay attacks. We also perform a formal verification using BAN logic to demonstrate that the proposed protocol meets the defined objectives. The proposed protocol can be used with any biometric authentication device to achieve the same objectives.

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“…Digital identities must be reliable in terms of strong coupling between the digital identity and physical individual (Chango, 2022). For stakeholders to trust the digital identity, the establishment of digital identity must be driven by robust, scalable, trustworthy, and secure information systems (Gavrilova, 2022;Seltsikas & O'Keefe, 2010).…”

Section: Introductionmentioning

confidence: 99%

Decentralized Identity Management Using Blockchain

Singla

Gupta

Aeron

et al. 2022

Journal of Global Information Management

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This article explores the usage of decentralised identity (DID) management using blockchain in global organisations to support secure usage of information resources. Blockchain as technology was initially introduced as a cryptocurrency and there have been challenges in its adoption for enterprise applications such as identity management. DID is emerging as one of the strong blockchain adoption use cases. Industry pioneers and users across domains have started exploring DID use cases, which help better protect their personal data and application access control as compared to traditional, central, or federated identity management models. In this exploratory work, the authors employ qualitative secondary case-based study research methodology to understand the challenges of the current digital identity management landscape and explore the possible benefits of DID as an emerging identity management paradigm. They propose a conceptual cube framework for analysing and studying various DID platforms thereby contributing to both the theory and practice of digitally secure identity.

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A Multifaceted Role of Biometrics in Online Security, Privacy, and Trustworthy Decision Making

Cited by 11 publications

References 50 publications

Emotion Detection From Micro-Blogs Using Novel Input Representation

Emotion Detection From Micro-Blogs Using Novel Input Representation

A protocol to establish trust on biometric authentication devices

Decentralized Identity Management Using Blockchain

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