Protecting Data Privacy in Analytics and Machine Learning

In this session, we will discuss a range of new emerging technologies for privacy and confidentiality in machine learning and data analytics. We will discuss how to put these technologies to work for databases and other data sources. When we think about developing AI responsibly, there’s many different activities that we need to think about. This session also discusses international standards and emerging privacy-enhanced computation techniques, secure multiparty computation, zero trust, cloud and trusted execution environments. We will discuss the “why, what, and how” of techniques for privacy preserving computing. We will review how different industries are taking opportunity of these privacy preserving techniques. A retail company used secure multi-party computation to be able to respect user privacy and specific regulations and allow the retailer to gain insights while protecting the organization’s IP. Secure data-sharing is used by a healthcare organization to protect the privacy of individuals and they also store and search on encrypted medical data in cloud. We will also review the benefits of secure data-sharing for financial institutions including a large bank that wanted to broaden access to its data lake without compromising data privacy but preserving the data’s analytical quality for machine learning purposes.