Antidatenbasierte

Antidatenbasierte is a German adjective used in technical and policy discussions to describe approaches, methods, or systems that are deliberately not based on extensive data collection or that seek to minimize dependence on data. The term is formed from anti- (against) plus daten (data) and basierte (based), and is typically found in phrases such as antidatenbasierte Ansätze or antidatenbasierte Entscheidungsfindung. Because it is not a standardized technical label, its exact meaning is context-dependent and may range from data-minimizing design principles to policy stances opposing data-heavy analytics.

Applications are common in data privacy, privacy-preserving AI, and data governance. Antidatenbasierte approaches emphasize reducing collection, limiting processing, using synthetic or aggregated data, or relying on rule-based or expert systems instead of machine learning trained on large datasets. They may also advocate evaluating models with privacy-preserving metrics or using simulation environments instead of real-world data.

Implications of antidatenbasierte thinking include improved privacy and reduced risk of data breaches, greater transparency, and alignment with data protection regulations. On the downside, these approaches can involve trade-offs in accuracy, generalization, and scalability, and may incur higher development costs or longer time-to-market for certain applications.

Related concepts include data minimization, privacy-by-design, differential privacy, synthetic data, and rule-based systems. The term antidatenbasierte is primarily used in German-language discourse on ethics, law, and information security rather than as a formal, widely adopted technical standard.