Guyu

No thanks as long as the Communists are around.

🎯 Key Takeaways for quick navigation: 00:02 📋 *Introduction to Differential Privacy and UN Datathon 2023* - Overview of the session and its focus on providing an intuitive understanding of differential privacy. - Mention of the UN Datathon and the importance of privacy technologies. 04:12 🌐 *Protecting Sensitive Data in the UN Datathon* - Explanation of the confidential computing environment and its role in safeguarding sensitive data. - Emphasis on sharing macro-level insights while preserving individual privacy. 08:04 📈 *The Importance of Differential Privacy: Massachusetts Health Data Case Study* - Discussion of the Massachusetts health data case study, highlighting the risks of improper data anonymization. - Illustration of how sensitive information can be reconstructed, emphasizing the need for privacy-enhancing technologies like differential privacy. 13:04 🔒 *Protecting Privacy in Data Competitions: The Netflix Prize and IMDb Example* - Explanation of the Netflix Prize competition and its potential privacy risks. - Introduction of the IMDb dataset and how metadata could be used to re-identify individuals. - Emphasis on the need to balance noise and utility in differential privacy. 19:43 🛡️ *The Mathematics of Differential Privacy* - Explanation of the mathematical foundation of differential privacy, including the concept of Epsilon. - Clarification of the sliding scale of privacy represented by Epsilon. - Mention of Epsilon-Delta differential privacy (not covered in detail for the Datathon). 24:19 🔧 *Introduction to Anular Platform and Differential Privacy* - Anular Platform overview, built on differential privacy tools. - Introduction to the Harvard op DP competition and upcoming UN Dataon. - How to log in and set up a Jupyter notebook for using Anular. 30:02 📊 *Differential Privacy Operations and Examples* - Explanation of applying differential privacy mechanisms to data. - Demonstrating differential privacy in practice with operations like apply map. - Discussing the balance between accuracy and privacy budget (Epsilon). 35:22 📚 *Resources and Tools for Differential Privacy* - Overview of available libraries and tools for differential privacy. - Mention of libraries like diffprivlib, smart noise, and op DP. - How to use notebooks and documentation for learning and implementing differential privacy techniques. 42:09 📈 *Differential Privacy in Machine Learning* - Explanation of how differential privacy is applied in machine learning, including neural networks and random forests. - Highlights the use of gradient clipping and noise addition in machine learning models. - Encouragement to explore documentation and notebooks for practical implementation. 48:54 📊 *Importing Open Data Sets into Anti-Granular* - You can import open data sets into Anti-Granular and use them during modeling. - The process involves creating a private data frame in Anti-Granular and importing the external data set. - You can then use record linkage techniques to combine the open data set with the private data frame. 56:39 🧩 *Integration of AWS Services in the Competition* - You can integrate AWS services while participating in the competition. - The competition doesn't restrict you from using AWS tools or other platforms to complement your work on the private data sets. - Your usage of AWS services can contribute to your entry in the Pet Track of the competition. 01:03:15 🕵️ *Dynamic Pipeline and Epsilon Measurement* - In the dynamic pipeline, you choose how much Epsilon you want to spend on each query. - Anti-Granular performs static and dynamic checks to ensure Epsilon is spent appropriately. - There is a hard cap on Epsilon spend to prevent excessive usage. 01:05:43 📈 *Indicator for Data in the Pet Track* - Data sets in the Pet Track contain real and sensitive information. - These data sets are tagged as Pet Track only because of their sensitivity. - Using data from the Pet Track automatically makes you eligible for the Pet Track prizes. Made with HARPA AI

Promo_SM 😎

Is this kind webinar open for everyone to participate? If so, how can I get the latest information to join the live session?

Thank for posting whole video! I eagerly looking forward to work with UN Big Data in someday.

*Promosm*

Congratulations on the successful completion. 👏🇺🇸🇺🇸🇺🇳🇺🇳🛰🛰🕊🕊👮‍♂️👮‍♂️--Madhavan Pallan UN/US TG(AI+QC)4Good #HNPW(#UN)

Good event. 🇺🇸🇺🇸🇺🇳🇺🇳🛰🛰👮‍♂️👮‍♂️⚖⚖

𝓅𝓇o𝓂o𝓈𝓂 😢

Awesome

Congratulations!

Thanks for informatic video!

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MIND FLOWING PROPH CSIS ÒF (HUN MAN )

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