Review of key‐binding‐based biometric data protection schemes

Sadhya, Debanjan; Singh, Sanjay Kumar; Chakraborty, Bodhi

doi:10.1049/iet-bmt.2015.0035

Cited by 18 publications

(9 citation statements)

References 114 publications

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“…To provide new insight into depression detection, there are different types of re-search exploring the relationship between mental wellbeing and language use. Sig-mund Freud [13], dating back to the earliest years of psychology, wrote about Freud-ian slips or linguistic errors to expose the authors' inner thoughts and feelings. Vari-ous approaches to the relationship between depression and its language have been established through the development of sociology and psycholinguistic theories.…”

Section: Related Workmentioning

confidence: 99%

“…For example, according to the cognitive theory of depression of Aaron. [ 13], affect-ed people appear to view themselves and their environment in mostly negative terms. Via derogatory words and first-person pronouns, they also express them-selves.…”

Section: Related Workmentioning

confidence: 99%

“…Recent studies examine the power of single features and their combinations such as N-grams + LIWC [13] or BOW+LDA and TF-IDF+LDA [14] to improve the accura-cy results. With almost 326 million active users and 90 million publicly distributed tweets to a wide audience, Twitter is one of the most popular social networking sites [14].…”

Section: Introductionmentioning

confidence: 99%

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Prediction of depression using Machine Learning and NLP approach

2021

IJICCN

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Today, for Internet users, micro-blogging has become a popular networking forum. Millions of people exchange views on different aspects of their lives. Thus, microblogging websites are a rich source of opinion mining data or Sentiment Analysis (SA) information. Because of the recent advent of microblogging, there are a few research papers dedicated to this subject. In our paper, we concentrate on Reddit.com, one of the leading microblogging sites, to explore the opinion of the public. We will demonstrate how to collect real-time Reddit data and use algorithms such as Support Vector Machine, KNN, and Multinomial Naive Bayes (MNB) for sentiment analysis or opinion mining purposes. We are able to assess positive and negative feelings using the algorithms selected above for the real-time Reddit depression info. The following experimental evaluations show that the algorithms used are accurate and can be used as an application for diagnosing the depression of individuals. After deployment User can write the content as input and after submitting the input text model API will be called from where result will come as User is going through depression or suicide. We worked with English in this post, but it can be used with any other language. English in this document, but it can be used for any other language this will be in future scope.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Prediction of depression using Machine Learning and NLP approach

2021

IJICCN

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“…Biometric cryptosystems [8][9][10][11][12][13] are designed to securely bind a cryptographic key with biometrics or generate a key from a biometric. Key binding biometric techniques [14] offer solutions to biometric-dependent key release and biometric template protection. The key binding biometric techniques have the following security advantages [1]: template protection, secure key release, pseudonymous authentication, prevention from several traditional attacks against biometric systems, biometric comparisons are performed indirectly, and more social acceptance.…”

Section: Motivationmentioning

confidence: 99%

Key binding biometrics‐based remote user authentication scheme using smart cards

Al-Saggaf

2017

IET biom.

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Remote user authentication schemes using smart cards provide solutions for securing user authentication. The schemes rely on passwords or biometric or both, which fall apart if the password is not kept secret. Additionally, the passwords can be stolen, lost, or forgotten and the stored biometric template is highly susceptible to a number of security and privacy attacks. This study presents a new biometric-based remote user authentication scheme using smart cards. In this study, the cryptographic key is concealed with a biometric template and discarded in the registration phase. During the login phase, the key is generated using a query biometric template in such a way that the key cannot be retrieved without a successful biometric verification. The strength of the proposed scheme is that it provides resistance to tampering and theft by way of a stored biometric template, privileged insider, and a smart card attacks and achieves some good properties such as it works without password (human memorised) and provides renewable property for the protected biometric template. Additionally, this scheme combines the good features of previous works to further capitalise on their sound security and design features. Security and performance analysis of the proposed scheme shows the advantage over other existing schemes.

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“…The ISO/IEC Standard 24745 on biometric information protection defines two major components for any BTP scheme: pseudonymous identifier and auxiliary data [6]. Based upon the generation technique of these two pieces of information, BTP schemes are broadly categorised as either cancellable biometrics [7] or biometric cryptosystems [8]. Our work is related to the later notion in which the biometric features are encoded using one‐way transformation functions.…”

Section: Introductionmentioning

confidence: 99%

Generation of cancellable locality sampled codes from facial images

2021

Self Cite

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Face is arguably the most common biometric trait that has been extensively utilised and thoroughly studied. Since this unimodal feature visually represents the identity of an individual, preserving the security of face-based authentication models is a prime concern. This work proposes a framework for generating cancellable templates from raw facial images. Our scheme is essentially based upon the notion of locality sensitive hashing (LSH), specifically on its locality sampled code (LSC) realisation. Facial features are initially extracted using the binarized statistical image features (BSIF) descriptor. These binary features are subsequently hashed using the random bit sampling mechanism of LSC. Finally, these local hashes are permanently stored in a noninvertible manner. We have empirically analysed the security requirements of unlinkability, non-invertibility, and revocability in our model. We have also validated our work over the benchmark AR, ORL, Yale, and CASIA-Facev5 databases under multiple scenarios. Among all the resulting cases, the best performance of our model was noted at a minimum EER of 2.69%, 4.45%, 1.2%, and 2.66% for the four data sets, respectively. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Review of key‐binding‐based biometric data protection schemes

Cited by 18 publications

References 114 publications

Prediction of depression using Machine Learning and NLP approach

Prediction of depression using Machine Learning and NLP approach

Key binding biometrics‐based remote user authentication scheme using smart cards

Generation of cancellable locality sampled codes from facial images

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