Data is everywhere. You interact with it: by calculating how much balance you have after buying something and looking at your SpO2 monitor while doing your favorite sport. The same data is being persisted somewhere, which can be used for various things. How can we make this data meet social and legal norms if we collect user data?

In this presentation, we'll talk about ways of sharing data and anonymizing to ensure data privacy with an example of an insurance data company or a competition site for instance:  Zindi. We will look into identifiers e.g personal identifiable information, sampling, k-anonymity, and differential privacy using machine learning techniques. The presentation will be helpful for data scientists, analysts, machine learning engineers, and software engineers working in health care and other disciplines.

Target Audience:

• Data Engineers
• Data Scientists
  
• Data Analysts
  
• Developers
  

Agenda:

1. Introduction
2. Data Privacy and Anonymization
3. Networking

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Africa's Talking community allows developers to learn skills for the modern-day African Developer. We are language and framework agnostic. All developers are welcome. This is where Africa's Talking developers community meets to build, learn and exchange knowledge.

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