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<!doctype html>
<html xmlns="http://www.w3.org/1999/html">
<head>
<!-- Meta Tags for SEO -->
<meta charset="UTF-8">
<meta name="description" content="Join the Introduction to Privacy-Preserving Machine Learning (PPML) workshop and learn how to protect sensitive data while leveraging the power of machine learning with PySyft and PyTorch.">
<meta name="keywords" content="Privacy-Preserving Machine Learning, PPML, PySyft, PyTorch, OpenMined, Federated Learning, Differential Privacy, Encrypted ML, Workshop">
<meta name="author" content="Valerio Maggio, OpenMined">
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<meta property="og:title" content="Introduction to Privacy-Preserving Machine Learning Workshop">
<meta property="og:description" content="Learn how to protect sensitive data in machine learning with the Introduction to PPML workshop, using PySyft and PyTorch.">
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<meta name="twitter:description" content="Discover how to protect sensitive data in machine learning with PySyft and PyTorch in this PPML workshop.">
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<title>Introduction to Privacy Preserving Machine Learning</title>
</head>
<body>
<header id="intro" class="align-items-center">
<div class="row align-items-center justify-content-center">
<div class="col-md-12">
<div class="head">
<h1 class="display-3 title">
Introduction to
<span style="color: #333333">Privacy Preserving</span>
Machine Learning
</h1>
<img class="logo" alt="Syft Logo"
src="./assets/imgs/OpenMined-Logo-Stacked-Light.png">
</div>
</div>
</div>
</header>
<main>
<div class='container' id="main-content">
<section id="about">
<div class="row">
<div class="col">
<h3><span>Overview</span></h3>
<p>This one-hour live webinar will introduce participants to the fundamentals of
Privacy Preserving Machine Learning (<code>PPML</code>). The session will
introduce key
PPML concepts such as Federated Learning, Differential Privacy, and
Homomorphic Encryption, giving participants a foundational understanding of
how to balance privacy and transparency with the effectiveness of ML models.
During the webinar, attendees will get practical insights into integrating
privacy-preserving techniques into ML workflows using PySyft - an open
source tool for secure and private machine learning.
</p>
<p>
<a href="https://forms.gle/UvCtBS8kh6mCJSyW8"
title="PPML Webinar Registration form" target="_blank">
<span style="font-size: 2.2rem"><strong>Register here</strong></span>
</a>
</p>
</div>
</div>
</section>
<section id="learning-goals">
<div class="row">
<div class="col-md-10">
<h3><span>Objectives</span></h3>
<ol start=''>
<li>Understand the core concepts and the importance of PPML.</li>
<li>Learn the basics of Federated Learning, Differential Privacy, and
Homomorphic Encryption.
</li>
<li>Learn how PySyft enables privacy-preserving Machine learning</li>
</ol>
</div>
</div>
</section>
<section id="datetime">
<div class="row">
<div class="col-md-10">
<h3><span> When & When</span></h3>
<ul>
<li><strong>Date:</strong> Thursday, 14 November 2024</li>
<li><strong>Time:</strong> 5 PM GMT / 6 PM CET / 12 PM EST / 9 AM PST </li>
<li><strong>Duration:</strong> 1 hour</li>
<li><strong>Location:</strong> Online (Information will be shared with attendees after registration) </li>
</ul>
</div>
</div>
</section>
<section id="audience">
<div class="row">
<div class="col-md-10">
<h3><span>Target Audience</span></h3>
<p>This webinar is designed for data scientists, AI practitioners, machine
learning engineers, and
developers interested in applying privacy-preserving techniques to their ML
models, especially
those working with sensitive data in domains like healthcare, finance, and
government.
</p>
</div>
</div>
</section>
<section id="agenda">
<div class="row">
<div class="col-md-10">
<h3><span>Webinar Agenda</span></h3>
<ul class="agenda">
<li><strong>Introduction to PPML and PySyft</strong> (10 minutes):
<ul>
<li>Importance of privacy in Machine learning.</li>
<li>Overview of PySyft as a tool for privacy-preserving ML development.</li>
</ul>
</li>
<li><strong>Core PPML Methods</strong> (30 minutes):
<ul>
<li><strong>Federated Learning</strong>: Training models across decentralized data
sources.
</li>
<li><strong>Differential Privacy</strong>: Adding noise to data to maintain
individual privacy in ML models.
</li>
<li><strong>Homomorphic Encryption</strong>: Secure computations on encrypted
data.
</li>
</ul>
</li>
<li><strong>PPML in PySyft with Structured Transparency</strong> (10 minutes):
<ul>
<li>PPML in practice using PySyft, and the Structured Transparency framework</li>
</ul>
</li>
<li><strong>Q&A Session</strong> (10 minutes):
<ul>
<li>Addressing participant questions and insights.</li>
</ul>
</li>
</ul>
</div>
</div>
</section>
<section id="takeaways">
<div class="row">
<div class="col">
<h3><span>Takeaways</span></h3>
<p>Participants will leave with a solid understanding of PPML, its importance, and how it can be
applied to machine learning or data science workflows.</p>
<p>This webinar is an ideal starting point for professionals seeking hands-on tools like PySyft to
ensure data privacy while leveraging the full potential of machine learning in sensitive
environments.</p>
</div>
</div>
</section>
<section id="materials">
<div class="row">
<div class="col">
<h3><span>Materials</span>
<img src="./assets/imgs/gh-logo.png" class="gh-logo"/></h3>
<p>Materials will be made available on this
<a href="https://github.com/openmined/intro-to-ppml"
title="Intro to PPML Repository" target="_blank">
<strong>Github Repository</strong>
</a>
in due course.
</p>
</div>
</div>
</section>
</div>
</main>
</body>
</html>