Personal data de-identification for data science tasks
The talk will cover the worst and the best ways to de-identify personal and sensitive data inside training datasets to make them compliant with GDPR, CCPA, and other data protection regulations effective in the US and EU, but still useful from a machine learning perspective. Also, key concepts of recent data protection acts and possible resources of the data breach will be discussed.
- Head of the data science department at 1touch.io, which is a platform for advanced data lifecycle management.
- She has extensive experience in continuous delivery of end-to-end NLP solutions mainly focused on performing multi-lingual analysis for high-load systems helping to enhance, summarize, and highlight specific properties of text data.
- LinkedIn, Medium