Doing Data Analysis on Sensitive Data (ft. Oblivious)

Thu Vu ยท Beginner ยท๐Ÿ“Š Data Analytics & Business Intelligence ยท2y ago
Skills: BI Tools53%

About this lesson

๐Ÿ† Check out Antigranular competition & win cash prizes up to โ‚ฌ5,500 ๐Ÿ‘‰ https://www.antigranular.com/competitions ๐Ÿ“” Sample notebook working with sensitive private data ๐Ÿ‘‰ https://shorturl.at/yKX89 ๐ŸŒŸ Master Python and Build Awesome AI Projects ๐Ÿ‘‰ https://python-course-earlybird.framer.website/?&utm_source=sensdata Receive top data science/ AI insights in your inbox ๐Ÿ‘‰ https://thu-vu.ck.page/49c5ee08f6 Hi everyone, in today's video we'll be talking about working with private and sensitive data. A lot of times data scientists do not work on the most valuable and "cool" datasets, simply because these datasets contain too sensitive individual information. I have encountered this many times at my work, and it's frustrating! In this video, I will give you a brief introduction to different techniques for handling senstive data, and how to implement them using Antigranular platform. Thank you for watching! ๐Ÿ˜‡ ๐Ÿ”‘ TIMESTAMPS ================================ 0:00 - Intro 1:00 - Antigranular/ Oblivious (sponsor) 1:22 - Overview of privacy-enhancing technologies 4:28 - Antigranular & competition ๐Ÿ† 5:58 - What is Differential Privacy? 6:51 - Sample notebook walkthrough 7:49 - What is Secure Enclave? 8:34 - Sample notebook walkthrough (cont.) 11:07 - Training a regression model on private data 11:46 - Outro ๐Ÿ‘ฉ๐Ÿปโ€๐Ÿ’ป COURSES & RESOURCES ================================ ๐Ÿ“– Google Advanced Data Analytics Certificate ๐Ÿ‘‰ https://imp.i384100.net/anK9zZ ๐Ÿ“– Google Data Analytics Certificate ๐Ÿ‘‰ https://imp.i384100.net/15v9y6 ๐Ÿ“– Learn SQL Basics for Data Science Specialization ๐Ÿ‘‰ https://imp.i384100.net/AovPnJ ๐Ÿ“– Excel Skills for Business ๐Ÿ‘‰ https://coursera.pxf.io/doPaoy ๐Ÿ“– Machine Learning Specialization ๐Ÿ‘‰ https://imp.i384100.net/RyjykN ๐Ÿ“– Data Visualization with Tableau Specialization ๐Ÿ‘‰https://imp.i384100.net/n15XWR ๐Ÿ“– Deep Learning Specialization ๐Ÿ‘‰ https://imp.i384100.net/zavBA0 ๐Ÿ“– Mathematics for Machine Learning and Data Science Specialization ๐Ÿ‘‰ https://imp.i384100.net/LXK

Original Description

๐Ÿ† Check out Antigranular competition & win cash prizes up to โ‚ฌ5,500 ๐Ÿ‘‰ https://www.antigranular.com/competitions ๐Ÿ“” Sample notebook working with sensitive private data ๐Ÿ‘‰ https://shorturl.at/yKX89 ๐ŸŒŸ Master Python and Build Awesome AI Projects ๐Ÿ‘‰ https://python-course-earlybird.framer.website/?&utm_source=sensdata Receive top data science/ AI insights in your inbox ๐Ÿ‘‰ https://thu-vu.ck.page/49c5ee08f6 Hi everyone, in today's video we'll be talking about working with private and sensitive data. A lot of times data scientists do not work on the most valuable and "cool" datasets, simply because these datasets contain too sensitive individual information. I have encountered this many times at my work, and it's frustrating! In this video, I will give you a brief introduction to different techniques for handling senstive data, and how to implement them using Antigranular platform. Thank you for watching! ๐Ÿ˜‡ ๐Ÿ”‘ TIMESTAMPS ================================ 0:00 - Intro 1:00 - Antigranular/ Oblivious (sponsor) 1:22 - Overview of privacy-enhancing technologies 4:28 - Antigranular & competition ๐Ÿ† 5:58 - What is Differential Privacy? 6:51 - Sample notebook walkthrough 7:49 - What is Secure Enclave? 8:34 - Sample notebook walkthrough (cont.) 11:07 - Training a regression model on private data 11:46 - Outro ๐Ÿ‘ฉ๐Ÿปโ€๐Ÿ’ป COURSES & RESOURCES ================================ ๐Ÿ“– Google Advanced Data Analytics Certificate ๐Ÿ‘‰ https://imp.i384100.net/anK9zZ ๐Ÿ“– Google Data Analytics Certificate ๐Ÿ‘‰ https://imp.i384100.net/15v9y6 ๐Ÿ“– Learn SQL Basics for Data Science Specialization ๐Ÿ‘‰ https://imp.i384100.net/AovPnJ ๐Ÿ“– Excel Skills for Business ๐Ÿ‘‰ https://coursera.pxf.io/doPaoy ๐Ÿ“– Machine Learning Specialization ๐Ÿ‘‰ https://imp.i384100.net/RyjykN ๐Ÿ“– Data Visualization with Tableau Specialization ๐Ÿ‘‰https://imp.i384100.net/n15XWR ๐Ÿ“– Deep Learning Specialization ๐Ÿ‘‰ https://imp.i384100.net/zavBA0 ๐Ÿ“– Mathematics for Machine Learning and Data Science Specialization ๐Ÿ‘‰ https://imp.i384100.net/LXK
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Chapters (10)

Intro
1:00 Antigranular/ Oblivious (sponsor)
1:22 Overview of privacy-enhancing technologies
4:28 Antigranular & competition ๐Ÿ†
5:58 What is Differential Privacy?
6:51 Sample notebook walkthrough
7:49 What is Secure Enclave?
8:34 Sample notebook walkthrough (cont.)
11:07 Training a regression model on private data
11:46 Outro
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