Schedule: Security and Privacy sessions

New regulations drive compliance, governance, and security challenges for big data. Infosec and security groups must ensure a secured and governed environment across workloads that span on-premises, private cloud, multicloud, and hybrid cloud. Mark Donsky, Lars George, Michael Ernest, and Ifigeneia Derekli outline hands-on best practices for meeting these challenges with special attention to CCPA. Read more.

From analyzing risk and detecting fraud to predicting payments and improving customer experience, take a deep dive into the ways data technologies are transforming the financial industry. Read more.

Bring your laptop, roll up your sleeves, and get ready to crunch some cybersecurity events with Apache Metron, an open source big data cybersecurity platform. Carolyn Duby walks you through how Metron finds actionable events in real time. Read more.

Anti-patterns are behaviors that take bad problems and lead to even worse solutions. In the world of data security and privacy, they’re everywhere. Over the past four years, data security and privacy anti-patterns have emerged across hundreds of customers and industry verticals—there's been an obvious trend. Steven Touw details five anti-patterns and, more importantly, the solutions for them. Read more.

The Apache Parquet community is working on a column encryption mechanism that protects sensitive data and enables access control for table columns. Many companies are involved, and the mechanism specification has recently been signed off on by the community management committee. Gidon Gershinsky explores the basics of Parquet encryption technology, its usage model, and a number of use cases. Read more.

With ML becoming more mainstream, the side effects of machine learning and AI on our lives become more visible. You have to take extra measures to make machine learning models fair and unbiased. And awareness for preserving the privacy in ML models is rapidly growing. Mikio Braun explores techniques and concepts around fairness, privacy, and security when it comes to machine learning models. Read more.

From the EU to California and China, more of the world is regulating how data can be used. Andrew Burt and Brenda Leong convene leading experts on law and data science for a deep dive into ways to regulate the use of AI and advanced analytics. Come learn why these laws are being proposed, how they’ll impact data, and what the future has in store. Read more.

Cybersecurity must find what it doesn’t know to look for. AI technologies led to the emergence of self-learning, self-defending networks that achieve this—detecting and autonomously responding to in-progress attacks in real time. Marcus Fowler examine these cyber-immune systems enable the security team to focus on high-value tasks, counter even machine-speed threats, and work in all environments. Read more.

The fear that comes along with new compliance requirements is overwhelming. Organizations don’t know where to start, what to fix, or what an auditor expects to see. Mark Hinely gives you an auditor's perspective on the newest security and privacy regulations, how your business can prepare for compliance, and what the audit looks like to an auditor. Read more.

Organizations often work with sensitive information such as social security and credit card numbers. Although this data is stored in encrypted form, most analytical operations require data decryption for computation. This creates unwanted exposures to theft or unauthorized read by undesirables. Matt Carothers, Jignesh Patel, and Harry Tang explain how homomorphic encryption prevents fraud. Read more.

Fine-grained data protection at a column level in data lake environments has become a mandatory requirement to demonstrate compliance with multiple local and international regulations across many industries today. Owen O'Malley dives into how column encryption in ORC files enables both fine-grain protection and audits of who accessed the private data. Read more.