Abstract Authorship Verification (AV) is a key area of research in digital text forensics, which addresses the fundamental question of whether two texts were written by the same person. Numerous computational approaches have been proposed over the last two decades in an attempt to address this challenge. However, existing AV methods often suffer from high complexity, low explainability, and especially from a lack of clear scientific justification. We propose a simpler method based on modeling the grammar of an author following Cognitive Linguistics principles. These models are used to calculate λ G (LambdaG): the ratio of the likelihoods of a document given the candidate’s grammar versus given a reference population’s grammar. Our empirical evaluation, conducted on 12 datasets and compared against seven baseline methods, demonstrates that LambdaG achieves superior performance, including against several neural network-based AV methods. LambdaG is also robust to small variations in the composition of the reference population and provides interpretable visualizations, enhancing its explainability. We argue that its effectiveness is due to the method’s compatibility with Cognitive Linguistics theories, predicting that a person’s grammar is a behavioral biometric.
Die Kontrolle des Alters einer Person kann im Internet verschiedene Gründe haben. Der rechtskonforme Zugriff auf FSK-18-Inhalte drängt sich auf, ebenso sind aber auch geschützte Kommunikationsräume für Minderjährige ein wichtiges Beispiel. Es sind zahlreiche Verfahren zur Zuordnung des Alters zu einer Person bekannt. Wir betrachten und strukturieren diese Verfahren und diskutieren ihren rechtlichen Hintergrund.
Taint analysis is a widely used technique to analyze Android apps, enabling the tracking of data flow within an app. A key step consists in identifying which methods act as SOURCEs (i.e., where a data of interest originates) and SINKs (where data might be exposed). Existing approaches typically fall into two categories: ➀ Handcrafted lists, which suffer from incompleteness and quickly become outdated; and ➁ Automated techniques, which, although scalable, over-approximate and produce many false positives, primarily due to the challenge of defining what qualifies as a SOURCE . While identifying SINKs is generally more straightforward (as they correspond to explicit exposure points), defining a universal criterion for what constitutes a SOURCE remains inherently challenging. For example, isMicrophoneMute () may not typically be considered a SOURCE , yet in specific contexts it could represent a significant privacy concern. This context dependence highlights the limitations of static, generic lists of SOURCE methods. We present TaskFlow , a novel LLM-driven framework for generating task-specific lists of SOURCE (or SINK ) methods aligned with specific analysis goals. By reasoning over API semantics and contextual usage, TaskFlow ➀ mitigates the noise commonly introduced by overly broad lists, leading to more precise taint analyses; and ➁ addresses the incompleteness of manual approaches.
State-of-the-art tools like FlowDroid have been proposed to detect data leaks in Android apps, but two main challenges persist: ① false alarms and ② undetected data leaks. One contributing factor to these challenges is that a tool such as FlowDroid relies on pre-defined lists of privacy-sensitive source and sink API methods. Generating such lists is complex; incomplete or inaccurate lists result in both false alarms (i.e., irrelevant data flows) and undetected data leaks. Additionally, data leaks are highly context-dependent. For instance, GPS data flowing from a navigation app is expected, but the same flow in a calculator app is suspicious. Even when FlowDroid identifies a source-to-sink path, it may not be relevant to privacy analysis, further increasing false alarms. To tackle these issues, we propose a novel approach named DamFlow, which, by combining backward taint analysis with context-aware anomaly detection, prevents a “flood” of irrelevant data flows while at the same time finding data leaks missed by existing approaches. Our evaluation demonstrates that DamFlow significantly reduces reported leaks per app while uncovering previously undetected leaks, enhancing FlowDroid’s practicality for real-world data leak detection.
Der vorliegende Beitrag untersucht datenschutzrechtliche Rechtsgrundlagen anhand dreier typischer Szenarien der Cybersicherheitsforschung und analysiert, unter welchen Voraussetzungen sie einschlägig sind.
