BioGames: a new paradigm and a behavioral biometrics collection tool for research purposes

Purpose The purpose of this paper is to present a new paradigm, named BioGames, for the extraction of behavioral biometrics (BB) conveniently and entertainingly. To apply the BioGames paradigm, the authors developed a BB collection tool for mobile devices named BioGames App. The BioGames App collects keystroke dynamics, touch gestures, and motion modalities and is available on GitHub. Interested researchers and practitioners may use it to create their datasets for research purposes. Design/methodology/approach One major challenge for BB and continuous authentication (CA) research is the lack of actual BB datasets for research purposes. The compilation and refinement of an appropriate set of BB data constitute a challenge and an open problem. The issue is aggravated by the fact that most users are reluctant to participate in long demanding procedures entailed in the collection of research biometric data. As a result, they do not complete the data collection procedure, or they do not complete it correctly. Therefore, the authors propose a new paradigm and introduce a BB collection tool, which they call BioGames, for the extraction of biometric features in a convenient way. The BioGames paradigm proposes a methodology where users play games without participating in an experimental painstaking process. The BioGames App collects keystroke dynamics, touch gestures, and motion modalities. Findings The authors proposed a new paradigm for the collection of BB on mobile devices and created the BioGames application. The BioGames App is an Android application that collects BB data on mobile devices and sends them to a database. The database design allows multiple users to store their sensor data at any time. Thus, there is no concern about data separation and synchronization. BioGames App is General Data Protection Regulation (GDPR) compliant as it collects and processes only anonymous data. Originality/value The BioGames App is a publicly available tool that combines the keystroke dynamics, touch gestures, and motion modalities. In addition, it uses a methodology where users play games without participating in an experimental painstaking process.

Users’ Privacy Attitudes towards the Use of Behavioral Biometrics Continuous Authentication (BBCA) Technologies: A Protection Motivation Theory Approach

Smartphone user authentication based on passwords, PINs, and touch patterns raises several security concerns. Behavioral Biometrics Continuous Authentication (BBCA) technologies provide a promising solution which can increase smartphone security and mitigate users’ concerns. Until now, research in BBCA technologies has mainly focused on developing novel behavioral biometrics continuous authentication systems and their technical characteristics, overlooking users’ attitudes towards BBCA. To address this gap, we conducted a study grounded on a model that integrates users’ privacy concerns, trust in technology, and innovativeness with Protection Motivation Theory. A cross-sectional survey among 778 smartphone users was conducted via Amazon Mechanical Turk (MTurk) to explore the factors which can predict users’ intention to use BBCA technologies. Our findings demonstrate that privacy concerns towards intention to use BBCA technology have a significant impact on all components of PMT. Further to this, another important construct we identified that affects the usage intention of BBCA technology is innovativeness. Our findings posit the view that reliability and trustworthiness of security technologies, such as BBCA are important for users. Together, these results highlighted the importance of addressing users’ perceptions regarding BBCA technology.

Biometric Systems De-Identification: Current Advancements and Future Directions

Biometric de-identification is an emerging topic of research within the information security domain that integrates privacy considerations with biometric system development. A comprehensive overview of research in the context of authentication applications spanning physiological, behavioral, and social-behavioral biometric systems and their privacy considerations is discussed. Three categories of biometric de-identification are introduced, namely complete de-identification, auxiliary biometric preserving de-identification, and traditional biometric preserving de-identification. An overview of biometric de-identification in emerging domains such as sensor-based biometrics, social behavioral biometrics, psychological user profile identification, and aesthetic-based biometrics is presented. The article concludes with open questions and provides a rich avenue for subsequent explorations of biometric de-identification in the context of information privacy.

Fine-Grained and Context-Aware Behavioral Biometrics for Pattern Lock on Smartphones

Pattern lock has been widely used in smartphones as a simple and effective authentication mechanism, which however is shown to be vulnerable to various attacks. In this paper, we design a novel authentication system for more secure pattern unlocking on smartphones. The basic idea is to utilize various behavior information of the user during pattern unlocking as additional authentication fingerprints, so that even if the pattern password is leaked to an attacker, the system remains safe and protected. To accommodate a variety of user contexts by our system, a context-aware module is proposed to distinguish any of such contexts (e.g., body postures when drawing the pattern) and use it to guide the authentication. Moreover, we design a polyline weighted strategy with overlapping based on the consistency of pattern lock, which analyzes the behavior information of the user during the unlock process in a fine-grained manner and takes an overall consideration the results of different polylines. Based on 14,850 samples collected from 77 participants, we have extensively evaluated the proposed system. The results demonstrate that it outperforms state-of-the-art implicit authentication based pattern lock approaches, and that each key module in our system is effective.