Hands-on data de-identification

Statistical Disclosure Control seeks to reduce the risk of confidential information disclosure by de-identifying them. Such de-identification is guaranteed through privacy-preserving techniques (PPTs). However, de-identified data usually results in loss of information, with a possible impact on data analysis precision and model predictive performance. This course covers the de-identification process which aims to protect the individual’s privacy while maintaining the interpretability of the data (i.e., its usefulness). The program includes:

• Introduction to Python
• Introduction to data privacy
• Predictive modeling
• Privacy-preserving techniques
• Training
• Current directions on data privacy