Explore privacy-preserving methods for building secure projects in this PyCon US talk by Rebeca Sarai. Delve into the growing importance of data anonymity and privacy laws, including PCI compliance, GDPR, and LGPD. Learn about the challenges of handling sensitive data across various environments and the risks of unauthorized access. Discover techniques for managing data while securing users' personal information, including anonymization and pseudonymization methods such as k-anonymity and differential privacy. Gain insights into protecting privacy in machine learning models and understand why solving the anonymity problem goes beyond simply replacing identifying information. Access accompanying slides and resources to further enhance your understanding of building secure, privacy-conscious projects.
Privacy-Preserving Methods - Building Secure Projects
Overview
Syllabus
Intro
Privacy-preserving methods: Building secure projects
You want to collect and release data that contains answers to sensitive questions
You want to collect and release data that eontains answers te sensitive questions
You want to make generalizations over a population
In the context of a database Given we perform some query on the database we remove a person from the database and the query does not change then that person's privacy is fully protected
You want to use prediction models with user's data
You want to update your model with user's data
Taught by
PyCon US
