A Mobile App for Assisting Users to Make Informed Selections in Security Settings for Protecting Personal Health Data: Development and Feasibility Study
Notice bibliographique
Résumé
BACKGROUND: On many websites and mobile apps for personal health data collection and management, there are security features and privacy policies available for users. Users sometimes are given an opportunity to make selections in a security setting page; however, it is challenging to make informed selections in these settings for users who do not have much education in information security as they may not precisely know the meaning of certain terms mentioned in the privacy policy or understand the consequences of their selections in the security and privacy settings. OBJECTIVE: The aim of this study was to demonstrate several commonly used security features such as encryption, user authentication, and access control in a mobile app and to determine whether this brief security education is effective in encouraging users to choose stronger security measures to protect their personal health data. METHODS: A mobile app named SecSim (Security Simulator) was created to demonstrate the consequences of choosing different options in security settings. A group of study participants was recruited to conduct the study. These participants were asked to make selections in the security settings before and after they viewed the consequences of security features. At the end of the study, a brief interview was conducted to determine the reason for their selections in the security settings. Their selections before and after the security education were compared in order to determine the effectiveness of the security education. The usability of the app was also evaluated. RESULTS: In total, 66 participants finished the study and provided their answers in the app and during a brief interview. The comparison between the pre- and postsecurity education selection in security settings indicated that 21% (14/66) to 32% (21/66) participants chose a stronger security measure in text encryption, access control, and image encryption; 0% (0/66) to 2% (1/66) participants chose a weaker measure in these 3 security features; and the remainder kept their original selections. Several demographic characteristics such as marital status, years of experience using mobile devices, income, employment, and health status showed an impact on the setting changes. The usability of the app was good. CONCLUSIONS: The study results indicate that a significant percentage of users (21%-32%) need guidance to make informed selection in security settings. If websites and mobile apps can provide embedded security education for users to understand the consequences of their security feature selection and the meaning of commonly used security features, it may help users to make the best choices in terms of security settings. Our mobile app, SecSim, offers a unique approach for mobile app users to understand commonly used security features. This app may be incorporated into other apps or be used before users make selections in their security settings.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».