Schedule: Privacy and Security sessions

Recent regulations in Europe (GDPR) and California (Consumer Privacy Act) have placed concepts like “user control” and “privacy-by-design” at the forefront for companies wanting to deploy ML. The good news is that there are new privacy-preserving tools and techniques – including differential privacy – that are becoming available for both business intelligence and ML applications.

• Data security and privacy: A recent white paper from the Hoover Institution observed that we are beginning to see the convergence of data privacy and security. This is an age when companies are guarding against the misuse of data, either by adversaries or by parties they presently trust but may not longer do so in the future: “Anyone, from a privacy perspective, can become an adversary, given enough time.”

• The use of data, analytics, and machine learning in security and cybersecurity.

• Privacy-preserving analytics.

• Secure and robust analytics, including secure machine learning and aspects of machine deception (such as machines deceiving machines, or people deceiving machines).