Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2404.13220 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866929326020100096 |
|---|---|
| author | Kleidermacher, Dave Arriaga, Emmanuel Wang, Eric Porst, Sebastian Jover, Roger Piqueras |
| author_facet | Kleidermacher, Dave Arriaga, Emmanuel Wang, Eric Porst, Sebastian Jover, Roger Piqueras |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_13220 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Security and Privacy Product Inclusion Kleidermacher, Dave Arriaga, Emmanuel Wang, Eric Porst, Sebastian Jover, Roger Piqueras Cryptography and Security Machine Learning 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. |
| title | Security and Privacy Product Inclusion |
| topic | Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2404.13220 |