Zahra Rahmani

Existing log anomaly detection methods are often slow, dependent on error-prone parsing, and use unrealistic evaluation protocols. This paper introduces K4 (Knowing the Unknown by Knowing only the Known), a fully unsupervised, parser-independent framework that transforms arbitrary log embeddings into compact four-dimensional descriptors—Precision, Recall, Density, Coverage—using efficient k-nearest neighbor statistics. Under a realistic online chunk-based evaluation protocol, K4 achieves state-of-the-art AUROC of 0.995–0.999 across HDFS, BGL, and Thunderbird datasets, with training under 4 seconds and inference as low as 4 μs.

The genomic domain stands to benefit greatly from advances in AI and data science, but increasing privacy and cybersecurity concerns necessitate robust solutions for sensitive collaborative research. This paper presents a practical deployment of a privacy-preserving framework for genomic research developed in collaboration with Lynx.MD, a secure health data collaboration platform, addressing challenges of enabling joint analysis of genomic data while mitigating data breach risks. The framework demonstrates scalable, privacy-preserving data sharing and analysis that maintains utility while satisfying rigorous security requirements in a real production environment.