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UID:pretalx-pyconde-pydata-2026-VJPQCR@pretalx.com
DTSTART;TZID=CET:20260416T101500
DTEND;TZID=CET:20260416T114500
DESCRIPTION:Data analysis and machine learning often involve sensitive info
 rmation. But how can we ensure that our analyses and releases do not inadv
 ertently reveal information about the individuals in our data? Traditional
  approaches such as anonymization or releasing only aggregate statistics h
 ave repeatedly proven insufficient.\n\nDifferential privacy is a mathemati
 cal framework that offers provable privacy guarantees while still enabling
  useful data analysis. In this tutorial\, we provide a hands-on introducti
 on to differential privacy\, covering key concepts relevant to understandi
 ng and applying it in practice. The focus will be on practical implementat
 ion rather than underlying theory.\n\nUsing interactive examples in Python
 \, we will explore the core ideas of differential privacy\, highlight its 
 attractive properties and limitations\, and demonstrate how to build priva
 cy-preserving analyses using OpenDP\, an open-source Python library for di
 fferential privacy. Participants will leave equipped to continue exploring
  differential privacy on their own. Familiarity with the basics of Python 
 programming is helpful\, but no prior knowledge of differential privacy is
  required.
DTSTAMP:20260412T142007Z
LOCATION:Dynamicum [Ground Floor]
SUMMARY:Your Data Is Leaking: A Hands-On Introduction to Differential Priva
 cy with OpenDP - Shlomi Hod\, Marcel Neunhoeffer
URL:https://pretalx.com/pyconde-pydata-2026/talk/VJPQCR/
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