The field of network diagnosis and synthetic data is rapidly evolving, with a focus on developing innovative methods for automating diagnosis, improving synthetic data quality, and enhancing the reliability of network systems. Recent research has explored the use of model-based diagnosis, quality-guided synthetic data utilization, and differentially private synthetic data release to address the challenges of network failures, data scarcity, and privacy concerns. Noteworthy papers in this area include:
- Model-Based Diagnosis: Automating End-to-End Diagnosis of Network Failures, which proposes a new paradigm for model-based network diagnosis that provides a systematic way to derive automated procedures for identifying the root cause of network failures.
- Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data, which introduces a quality-guided synthetic data utilization scheme that refines synthetic data quality during task model training.
- Differentially Private Synthetic Data Release for Topics API Outputs, which develops a novel methodology to construct synthetic API outputs that are simultaneously realistic and provide strong privacy protections.