The Limitations of Metrics: Why Data Alone Fails to Provide Meaningful Insights
Metrics and data alone are insufficient for driving meaningful business decisions; they must be accompanied by a deeper understanding of the underlying context and narrative.
Linked Open Data Query-Logs Analytics Solution
Linked Open Data query-logs provide valuable insights, but trust and quality issues require a comprehensive end-to-end solution for effective analytics.
Private Count Release: A Simple and Scalable Approach for Private Data Analytics
Private Count Release bietet eine einfache und skalierbare Methode für private Datenanalysen, die auf Differential Privacy basiert.
Linked Open Data Query-Logs Analytics: End-to-End Solution
Linked Open Data query-logs provide valuable insights when curated and analyzed with a trust-based approach.
Privacy-Preserving Sharing of Data Analytics Runtime Metrics for Performance Modeling
The author presents a privacy-preserving approach for sharing runtime metrics based on differential privacy and data synthesis to maintain performance prediction accuracy. The main thesis is that synthetic training data can be used effectively to preserve privacy while maintaining model accuracy.
Private Count Release: A Simple and Scalable Approach for Private Data Analytics by Ryan Rogers
The author presents a novel approach for releasing accurate counts with differential privacy, highlighting the inefficiencies of existing methods that rely on user contribution bounds.
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