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SUMMARY:Keeping data private in real-time pipelines - Olena Kutsenko
DTSTART;TZID=Europe/Zurich:20261023T121000
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DTSTAMP:20261111T040116Z
UID:pretalx-swiss-python-summit-2026-T9DXZE@talks.python-summit.ch
DESCRIPTION:We all love real-time data — clicks\, payments\, rides\, mes
 sages — but most of it comes with a catch: it contains personal informat
 ion we’re not supposed to leak\, such as names\, emails\, locations\, or
  even small clues that can identify someone. The challenge: how do we keep
  streaming data useful and safe at the same time?\n\nIn this talk\, we’l
 l explore practical ways to protect privacy in streaming systems using Apa
 che Kafka\, Apache Flink\, and Apache Iceberg. We’ll cover:\n- simple tr
 icks like masking and tokenizing PII\;\n- why “anonymous” data often i
 sn’t anonymous (the re-identification problem)\;\n- techniques like buck
 eting\, k-anonymity\, and adding noise\;\n- how to balance privacy with da
 ta utility (too much hiding makes data useless).\n\nAlong the way\, we’l
 l look at real-world stories: from public data leaks to surprising deanony
 mization attacks\, and show live demos of pipelines that anonymize data be
 fore it’s written to storage.\nIf you’ve ever wondered how to build pr
 ivacy-aware pipelines\, this talk will give you practical patterns you can
  use right away.
LOCATION:Aula 4.101
URL:https://talks.python-summit.ch/swiss-python-summit-2026/talk/T9DXZE/
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