Secure and Privacy Preserving Method for Biometric Template Protection using Fully Homomorphic Encryption

Jindal, Arun Kumar; Shaik, Imtiyazuddin; Vasudha, Vasudha; Chalamala, Srinivasa Rao; Ma, Rajan; Lodha, Sachin

doi:10.1109/trustcom50675.2020.00149

Cited by 17 publications

(13 citation statements)

References 33 publications

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“…Current work in this area tends to focus on finding a balance between speeding up HE operations (e.g., by quantising/binarising the face templates or reducing their dimensionality) while simultaneously minimising losses in the resulting recognition accuracy (e.g., by trying to encrypt face templates in their original -usually floating-point -domain). Although [6]- [12] demonstrate important advances towards these goals for HE applied to both face verification (1-to-1 matching) [6], [7], [9] and identification (1-to-N matching) [8], [10]- [12], the greatest disadvantage of HE is that the encrypted templates remain secure only insofar as the corresponding decryption key remains secret; if an adversary gains access to this secret key, they can completely reverse the protection algorithm to obtain the original (unencrypted) template.…”

Section: A Non-nn Face Btp Methodsmentioning

confidence: 99%

“…The most popular FaceNet model repository appears to be that of David Sandberg (https://bit.ly/3BFc2Fm), but this is not the only one (e.g., https://bit.ly/2WJTogQ presents another version). So, when authors do not point to a particular FaceNet implementation [8], [9], [25], [29] in Table V), the reproducibility of their work is reduced. In general, however, Table V shows that only about a third of the studied face BTP works did not provide a clear indication of where their adopted DNN models/architectures came from, whereas the remaining two thirds provided either a direct link or sufficient information to locate the adopted DNN (e.g., a citation of the DNN's paper, where a public repository link is provided).…”

Section: Methods Type Referencementioning

confidence: 99%

“…Non-NN [6] Accuracy (%) [7] TAR @ FAR = {0.01, 0.1, 1} (%) [8] DET (FNIR vs. FPIR) (%) ≈ [9] GAR @ FAR = {0.01, 0.1, 1} (%) [10] Rank-1 recognition rate (%), DET (FNIR vs. FPIR) (%) ≈ ≈ EER (%), FNIR @ FPIR = 0.1 (%) [11] Rank-1 accuracy (%) ≈…”

Section: Methods Type Referencementioning

confidence: 99%

“…For[29],[50], we are referred to[36], on which[29],[50] are based 9. We are referred to[101], where[30] was applied to iris biometrics.…”

mentioning

confidence: 99%

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Biometric Template Protection for Neural-Network-based Face Recognition Systems: A Survey of Methods and Evaluation Techniques

Hahn¹,

Marcel²

2021

Preprint

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As automated face recognition applications tend towards ubiquity, there is a growing need to secure the sensitive face data used within these systems. This paper presents a survey of biometric template protection (BTP) methods proposed for securing face "templates" (images or representative features) in neural-network-based face recognition systems. The BTP methods are categorised into two types: Non-NN and NN-learned. Non-NN methods use a neural network (NN) as a feature extractor, but the BTP part is based on a non-NN algorithm applied at either image-level or feature-level. In contrast, NN-learned methods specifically employ a NN to learn a protected template from the unprotected face image or features. We present examples of Non-NN and NN-learned face BTP methods from the literature, along with a discussion of the two categories' comparative strengths and weaknesses. We also investigate the techniques used to evaluate these BTP methods, in terms of the three most common BTP criteria: "recognition accuracy", "irreversibility", and "renewability/unlinkability". As expected, the recognition accuracy of protected face recognition systems is generally evaluated using the same (empirical) techniques employed for evaluating standard (unprotected) biometric systems. On the contrary, most irreversibility and renewability/unlinkability evaluations are found to be based on theoretical assumptions/estimates or verbal implications, with a lack of empirical validation in a practical face recognition context. We recommend, therefore, a greater focus on empirical evaluation strategies, to provide more concrete insights into the irreversibility and renewability/unlinkability of face BTP methods in practice. Additionally, an exploration of the reproducibility of the studied BTP works, in terms of the public availability of their implementation code and evaluation datasets/procedures, suggests that it would currently be difficult for the BTP community to faithfully replicate (and thus validate) most of the reported findings. So, we advocate for a push towards reproducibility, in the hope of furthering our understanding of the face BTP research field.

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Section: A Non-nn Face Btp Methodsmentioning

confidence: 99%

Section: Methods Type Referencementioning

confidence: 99%

Section: Methods Type Referencementioning

confidence: 99%

“…For[29],[50], we are referred to[36], on which[29],[50] are based 9. We are referred to[101], where[30] was applied to iris biometrics.…”

mentioning

confidence: 99%

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Biometric Template Protection for Neural-Network-based Face Recognition Systems: A Survey of Methods and Evaluation Techniques

Hahn¹,

Marcel²

2021

Preprint

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“…EEG-biometric-based identification and authentication represents a significant and evolving field of research and practice [86], as it entails several benefits from both a security and a usability point of view. Specifically, biometrics can create high entropies of the secret biometric data used for authentication, they can minimize administration expenses, they offer convenience to end-users compared to traditional knowledge-based (e.g., passwords) and token-based (e.g., Time-based One-Time Passcodes -TOTP) solutions, and they provide a sense of technological modernity to the end-users [87,88]. Common approaches for biometric-based authentication are relying on the end-users' physical (e.g., fingerprint, iris, face, voice, etc.)…”

Section: Challenges and Future Research Directionsmentioning

confidence: 99%

A Review of EEG-Based User Authentication: Trends and Future Research Directions

Fidas

Lyras²

2023

IEEE Access

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The employment of Electroencephalography (EEG) in the User Authentication (UA) scientific research has recently unlocked state-of-the-art experimentation, aiming at identifying and authenticating individuals given their brainiac activity within specific contexts of use. Indeed, utilizing EEG signals that are derived from brainiac activities can be used for tackling existing UA security threats such as shoulder surfing, thus providing a novel solution to contemporary security problems in traditional knowledge-based user authentication. In this survey, we aim to complete previous literature surveys by providing a systematic classification and presentation of existing research that is based on the following pillars: a) the user experimental setup, with an emphasis on the applied EEG-acquisition protocols (e.g., rest, external stimuli driven, mental and hybrid); b) the artificial intelligence techniques employed and finally c) the security and privacy preservation aspects. The reviewed papers cover a broad area of experiment protocols and various algorithms used for EEG signal classification. Moreover, most cited works include results from more than one experiments with different approaches and configurations. This leads to a discussion on best practices for EEG-based User Authentication and conclusions suggesting future research directions that consists, among others, of considering homomorphically encrypted biometric templates for information leakage prevention through federated learning approaches in decentralized architectures. We anticipate that the present literature review will provide a roadmap for future research by considering efficiently and effective EEG-based User Authentication methods while at the same time preserving privacy.

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WiP: Privacy Enabled Biometric Authentication Based on Proof of Decryption Techniques

Syed

Shaik

Emmadi

et al. 2021

Information Systems Security

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Secure and Privacy Preserving Method for Biometric Template Protection using Fully Homomorphic Encryption

Cited by 17 publications

References 33 publications

Biometric Template Protection for Neural-Network-based Face Recognition Systems: A Survey of Methods and Evaluation Techniques

Biometric Template Protection for Neural-Network-based Face Recognition Systems: A Survey of Methods and Evaluation Techniques

A Review of EEG-Based User Authentication: Trends and Future Research Directions

WiP: Privacy Enabled Biometric Authentication Based on Proof of Decryption Techniques

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