Are Privacy Enhancing Technologies within reach for most businesses? What are the legal and market implications of their widespread use?
Katharine Jarmul is a privacy activist and data scientist focused on privacy and security in data science workflows. She’s a principal data scientist at Thoughtworks and has worked at various companies in the US and Germany before that. She is also a frequent keynote speaker at software and AI conferences.
Katharine has recently published “Practical Data Privacy” (O’Reilly, 2023), in which she provides a deep dive of Privacy Enhancing Technologies (“PET”), including detailed answers to increasingly common questions: How can we actually anonymize data? How does federated learning work? Can we already leverage Homomorphic Encryption to run analysis or work with data even while it is encrypted? How can we compare and pick the most appropriate PETs? Can we use open source libraries?
In our discussion:
- Can we bring Privacy Enhancing Technologies down to earth for smaller companies to understand and apply them on a regular basis? Are they otherwise the monopoly of Big Tech, and does this mean that a company like Meta ends up becoming the unlikely poster child for Privacy by Design?
- Can we really speak of a common ethical framework for AI or GenAI? How does a US/Western Europe ethical framework fit within African or Asian cultures?
- Can we break the convenience barrier when it comes to individual control?