Dates
Conferences
Tags
Sort by:
Most recent
Conference: Transform X 2021
Authors: Chris Hazard
2021-10-07
tldr - powered by Generative AI
The presentation discusses the importance of privacy in data synthesis and the use of synthetic data to enhance privacy while unlocking the value of data. It also highlights the challenges and potential risks associated with synthetic data and the need for proper application of privacy techniques.
- Privacy affects behavior and is crucial for building trust and value in a brand
- Synthetic data can be used to unlock the value of data while maintaining privacy
- Proper application of privacy techniques is necessary to avoid potential risks and challenges associated with synthetic data
- Synthetic data can be generated using various techniques such as Bayesian networks and GANs
- Synthetic data sets should be generated with distributions that have the same analytic outcome as the original data
- Synthetic data sets should be generated with caution to avoid leaking privacy
- Synthetic data sets can be generated multiple times with different levels of fidelity as long as privacy is maintained
- Validation of privacy and value is necessary when using synthetic data