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Research on keystroke dynamics has the good potential to offer continuous authentication that complements conventional authentication methods in combating insider threats and identity theft before more harm can be done to the genuine users. Unfortunately, the large amount of data required by free-text keystroke authentication often contain personally identifiable information, or PII, and personally sensitive information, such as a user's first name and last name, username and password for an account, bank card numbers, and social security numbers. As a result, there are privacy risks associated with keystroke data that must be mitigated before they are shared with other researchers. We conduct a systematic study to remove PII's from a recent large keystroke dataset. We find substantial amounts of PII's from the dataset, including names, usernames and passwords, social security numbers, and bank card numbers, which, if leaked, may lead to various harms to the user, including personal embarrassment, blackmails, financial loss, and identity theft. We thoroughly evaluate the effectiveness of our detection program for each kind of PII. We demonstrate that our PII detection program can achieve near perfect recall at the expense of losing some useful information (lower precision). Finally, we demonstrate that the removal of PII's from the original dataset has only negligible impact on the detection error tradeoff of the free-text authentication algorithm by Gunetti and Picardi. We hope that this experience report will be useful in informing the design of privacy removal in future keystroke dynamics based user authentication systems.
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This content will become publicly available on November 30, 2026
Keystroke Dynamics: Concepts, Techniques, and Applications
Reliably identifying and verifying subjects remains integral to computer system security. Various novel authentication techniques, such as biometric authentication systems, have been developed in recent years. This article provides a detailed review of keystroke-based authentication systems and their applications. Keystroke dynamics is a behavioral biometric that is emerging as an important tool for cybersecurity as it promises to be nonintrusive and cost-effective. In addition, no additional hardware is required, making it convenient to deploy. This survey covers novel keystroke datasets, state-of-the-art keystroke authentication algorithms, keystroke authentication on touch screen and mobile devices, and various prominent applications of such techniques beyond authentication. The article covers all the significant aspects of keystroke dynamics and can be considered a reference for future researchers in this domain. The article includes a discussion of the latest keystroke datasets, providing researchers with an up-to-date resource for analysis and experimentation. In addition, this survey covers the state-of-the-art algorithms adopted within this domain, offering insights into the cutting-edge techniques utilized for keystroke analysis. Moreover, this article explains the diverse applications of keystroke dynamics, particularly focusing on security, verification, and identification uses. Beyond these crucial areas, we mention additional applications where keystroke dynamics can be applied, broadening the scope of understanding regarding its potential impact across various domains. Unlike previous survey articles, which typically concentrate on specific aspects of keystroke dynamics, our comprehensive analysis presents all relevant areas within this field. By introducing discussions on the latest advances, we provide readers with a thorough understanding of the current landscape and emerging trends in keystroke dynamics research. Furthermore, this article presents a summary of future research opportunities, highlighting potential areas for exploration and development within the realm of keystroke dynamics. This forward-looking perspective aims to inspire further inquiry and innovation, guiding the trajectory of future studies in this dynamic field.
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- Award ID(s):
- 2526924
- PAR ID:
- 10610430
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- ACM Computing Surveys
- Volume:
- 57
- Issue:
- 11
- ISSN:
- 0360-0300
- Page Range / eLocation ID:
- 1 to 35
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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