Shifting Privacy Left Podcast

This week, we welcome Lipika Ramaswamy, Senior Applied Scientist at Gretel AI, a privacy tech company that makes it simple to generate anonymized and safe synthetic data via APIs. Previously, Lipika worked as a Data Scientist at LeapYear Technologies, and was the Machine Learning Researcher at Harvard University's Privacy Tools Project.

Lipika’s interest in both machine learning and privacy comes from her love of math and things that can be defined with equations. Her interest was piqued in grad school and accidentally walked into a classroom holding a lecture on Applying Differential Privacy for Data Science. The intersection of data combined with the privacy guarantees that we have available today has kept her hooked ever since.

There's a lot to unpack when it comes to synthetic data & privacy guarantees, as she takes listeners on a deep dive of these compelling topics. Lipika finds elegant how privacy assurances like differential privacy revolve around math and statistics at their core. Essentially, she loves building things with 'usable privacy' & security that people can easily use. We also delve into the metrics tracked in the Gretel Synthetic Data Report, which assesses both 'statistical integrity' & 'privacy levels' of a customer's training data.

Topics Covered:

  • The definition of 'synthetic data,' & good use cases
  • The process of creating synthetic data
  • How to ensure that synthetic data is 'privacy-preserving'
  • Privacy problems that may arise from overtraining ML models
  • When to use synthetic data rather than other techniques like tokenization, anonymization, aggregation & others
  • Examples of good use cases vs poor use cases for using synthetic data
  • Common misperceptions around synthetic data
  •'s approach to 'privacy assurance,' including a focus on 'privacy filters,' which prevent some privacy harms outputted by LLMs
  • How to plug into the 'synthetic data' community
  • Who bears the responsibility for educating the public about new technology like LLMs and potential harms
  • Highlights from's _synthesize 2023 conference

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