mlsanityData to 7 Oct 2026

Security and Privacy Product Inclusion

arXiv 2404.13220 ↗First posted 20 Apr 2024cs.CR cs.LGcited by 0

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Abstract (arXiv)

In this paper, we explore the challenges of ensuring security and privacy for users from diverse demographic backgrounds. We propose a threat modeling approach to identify potential risks and countermeasures for product inclusion in security and privacy. We discuss various factors that can affect a user's ability to achieve a high level of security and privacy, including low-income demographics, poor connectivity, shared device usage, ML fairness, etc. We present results from a global security and privacy user experience survey and discuss the implications for product developers. Our work highlights the need for a more inclusive approach to security and privacy and provides a framework for researchers and practitioners to consider when designing products and services for a diverse range of users.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 22 references that are in the map. First posted on arXiv 20 Apr 2024.

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9 of 22 references.

References · 9

  1. 2019Mitigate Bias in Face Recognition using Skewness-Aware Reinforcement Learningcited by 145
  2. 2018TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Timecited by 108
  3. 2020Demographic Bias in Biometrics: A Survey on an Emerging Challengecited by 93
  4. 2020SensitiveLoss: Improving Accuracy and Fairness of Face Representations with Discrimination-Aware Deep Learningcited by 50
  5. 2018An Experimental Evaluation of Covariates Effects on Unconstrained Face Verificationcited by 28
  6. 2020Demographic Bias in Presentation Attack Detection of Iris Recognition Systemscited by 13
  7. 2022Fairness in Face Presentation Attack Detectioncited by 13
  8. 2022Dataset Bias in Android Malware Detectioncited by 3
  9. 2022On Demographic Bias in Fingerprint Recognitioncited by 2

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