Schedule: Platform security and cybersecurity sessions

Graph databases are the fastest growing category in data management. However, most graph queries only traverse two hops in big graphs due to limitations in most graph databases. Real-world applications require deep link analytics that traverse far more than three hops. Yu Xu offers an overview of a fraud detection system that manages 100 billion graph elements to detect risk and fraudulent groups. Read more.

Recent headline-grabbing data breaches demonstrate that protecting data is essential for every enterprise. The best-of-breed approach for big data is HDFS configured with Transparent Data Encryption (TDE). However, TDE can be difficult to configure and manage—issues that are only compounded when running on Docker containers. Thomas Phelan explores these challenges and how to overcome them. Read more.

Clinical collaboration benefits from pooling data to train models from large datasets, but it's hampered by concerns about sharing data. Balasubramanian Narasimhan, John-Mark Agosta, and Philip Lavori outline a privacy-preserving alternative that creates statistical models equivalent to one from the entire dataset. Read more.

How should you best debug a security data science system: change the ML approach, redefine the security scenario, or start over from scratch? Ram Shankar answers this question by sharing the results of failed experiments and the lessons learned when building ML detections for cloud lateral movement, identifying anomalous executables, and automating incident response process. Read more.