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Auflistung nach Schlagwort "log files"

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  • Konferenzbeitrag
    Pseudonymizing Log Entries with time-selective Disclosure
    (Workshops der INFORMATIK 2018 - Architekturen, Prozesse, Sicherheit und Nachhaltigkeit, 2018) Sonntag, Michael
    Centralized logging of entries containing personally-identifiable data, like IP addresses, is common. However, this chances that persons other than the operator of the individual server might obtain access to these logs and then disclose or use them. Additionally, the GDPR recommends as a security measure pseudonymization, i.e. splitting the information into two parts. This article describes a method to pseudonymize personal information in elements stored in a time series. After a predetermined time, the information can be automatically anonymized without requiring any changes in the stored entries themselves. Additionally, some statistical analyses remain possible, as the same values are encoded with the same pseudonym. It is also possible to disclose an arbitrary time period from within the log file: everything after the start time and before the end time can be de-pseudonymized, but the rest of the data remains anon-/pseudonymous.
  • Konferenzbeitrag
    Track every move of your students: log files for Learning Analytics from mobile screen recordings
    (DeLFI 2018 - Die 16. E-Learning Fachtagung Informatik, 2018) Krieter, Philipp; Breiter, Andreas
    One of the main data sources for Learning Analytics are Learning Management Systems (LMS). These log files are limited though to interactions within the LMS and cannot take into account interactions of students in other applications and software in a digital learning environment. In this paper, we present an approach for generating log files based on mobile screen recordings as a data source for Learning Analytics. Logging mobile application usage is limited to rather general system events unless you have access to the source code of the operating system or applications. To address this we generate log files from mobile screen recordings by applying computer vision and machine learning methods to detect individually defined events. In closing, we discuss how these log files can be used as a data source for Learning Analytics and relevant ethical concerns.