How Differential Privacy Changed The World, and What The Math Really Means

Rumanu Bhardwaj (~rumanu)




A walkthrough how differential privacy became the industry standard for data privacy, pointing out the mathematical elements ensuring this robustness.

[7 Minutes] Exploration of how personalized permission marketing advertising systems created the need to protect consumer privacy

[4 Minutes] The technical definition of Differential Privacy

[7 Minutes] Exploration of the mathematical components of Differential Privacy

[5 Minutes] A brief discussion of approximations of Differential Privacy and when to use them

[2 Minutes] Examples in Python Libraries that can be used to implement DIfferential Privacy, e.g. PyDP

[5 Minutes] Q&A


Familiarity with statistical distributions and python programming is recommended to the participants.

Speaker Info:

Rumanu is deeply passionate about bringing privacy to data infrastructures globally, and studies the intersection of laws in that regard with privacy preserving technologies and their integration to reccommender systems and customized advertising. She was born a data scientist in the circuit via PyData and other developer circle meetups and likes to be involved with the community at large. She is excited to be a first time in person PyCon India speaker and connect with the community.

Speaker Links:

Section: Scientific Computing
Type: Talks
Target Audience: Intermediate
Last Updated: