Data Privacy and Security

Many of the data analytics applications are related to people, therefore privacy and security need special attention. Specific areas related to data privacy and security are:

• Data Anonymization: When data needs to be disclosed to third parties, sensitive data and personally identifying information needs to be separated so that they cannot be linked back which is the task of anonymization.
• Privacy Preserving Data Analysis: When data analytics will be performed on sensitive data, one option is to do that on encrypted data or by adding noise to sensitive data.
• Differential Privacy: Differential privacy is a mathematical definition widely used for quantifying data privacy. Methods that adhere to this definition protect individuals' data privacy while allowing for collective analyses of these data. Developed by Cynthia Dwork in the early 2000s, this approach provides user-level privacy by preventing the leakage of sensitive information while ensuring that databases remain available for useful analyses.