Project Overview
AnKSeK develops privacy-preserving methods for the automatic calibration of distributed sensor networks. It ensures that calibration data does not compromise user or location privacy.
Our Goal
Develop anonymization algorithms for calibration in large IoT systems. Balance data utility and privacy in sensor networks. Integrate methods into existing TECO calibration frameworks.
Highlights
Combines machine learning with privacy engineering. Addresses regulatory challenges (GDPR compliance). Implemented under Software Campus.
Impact
Enables scalable, privacy-compliant IoT calibration. Protects personal data while maintaining high calibration accuracy. Paves the way for responsible AI in sensor infrastructures.
