Biometric System De-identification: Concepts, Applications, and Open Problems

Shopon,; Bari, A. S. M. Hossain; Bhatia, Yajurv; Narayanaswamy, Pavan Karkekoppa; Tumpa, Sanjida Nasreen; Sieu, Brandon; Gavrilova, Marina L.

doi:10.1007/978-3-030-83620-7_17

Cited by 8 publications

(2 citation statements)

References 70 publications

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“…De-identification is becoming an increasingly important technique in the protection of personal data, alongside other techniques such as encryption and cancelability [14]. It refers to removing identifying information from data sets while preserving their utility for analysis [18]. The de-identification of biometric data is necessary to protect privacy and ensure that the data can be used for research and other applications without violating ethical and legal regulations.…”

Section: Introductionmentioning

confidence: 99%

Emotion-Aware Face De-identification with Generative Adversarial Networks

Shopon,

Gavrilova

2024

Preprint

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This paper presents an innovative end-to-end emotion-preserving de-identification approach utilizing a Generative Adversarial Network (GAN), employing the StyleGAN architecture. The method produces natural-looking de-identified images by generating a synthetic face image dataset and utilizing the DeepFace model for gender classification and representative image selection. An enhanced SimSwap framework enhances emotion preservation, accompanied by a novel loss function tailored to emotional expression preservation. The deep face model serves to classify and recognize emotional expressions in both original and de-identified swapped images. Explicit preservation of emotional expressions during face-swapping is achieved through attribute preservation loss minimization. The paper conducts a detailed ablation study to demonstrate the superiority of the proposed GAN components. The method exhibits superior performance in accuracy and emotion preservation compared to recent face de-identification methods.

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Section: Introductionmentioning

confidence: 99%

Emotion-Aware Face De-identification with Generative Adversarial Networks

Shopon,

Gavrilova

2024

Preprint

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“…This new phase of industrial revolution poses huge responsibility to business, their IT systems and IT security practitioners to ensure their data, especially PID, are processed in a secure manner. Shopon et al (2022) noted that IIDTRC is one of the most common privacy invasions that can be done through leakage of multi-modal biometric information within business organisations.…”

Section: Introductionmentioning

confidence: 99%

The Application of Role-Based Framework in Preventing Internal Identity Theft Related Crimes: A Qualitative Case Study of UK Retail Companies

Okeke

Eiza

2022

Inf Syst Front

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This paper aims to examine the challenges of preventing internal identity theft related crimes (IIDTRC) in the UK retail sector. Using an in-depth multiple case studies of a selected number of cross-functional management teams in the UK retail companies, management roles were analysed. We used semi-structured interview as a qualitative data collection technique and used Nvivo aided thematic analysis and interpretivism underpinned by Role-Based Framework (RBF) for analysis. Our findings revealed that vagueness of roles and lack of clarity in sharing data security responsibilities are the major challenges of preventing IIDTRC in UK retail companies. We suggest an application of RBF which provides a conceptual analysis for cross-functional management team to address the challenges of preventing IIDTRC. RBF enables clarity of shared roles where both information security and crimes prevention teams work in unison is required to prevent IIDTRC to maximise internal data security. Contributions for policymakers are offered in this paper.

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I Got Your Emotion: Emotion Preserving Face De-identification Using Injection-Based Generative Adversarial Networks

Shopon,

Gavrilova

2023

Advances in Visual Computing

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Biometric System De-identification: Concepts, Applications, and Open Problems

Cited by 8 publications

References 70 publications

Emotion-Aware Face De-identification with Generative Adversarial Networks

Emotion-Aware Face De-identification with Generative Adversarial Networks

The Application of Role-Based Framework in Preventing Internal Identity Theft Related Crimes: A Qualitative Case Study of UK Retail Companies

I Got Your Emotion: Emotion Preserving Face De-identification Using Injection-Based Generative Adversarial Networks

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