1/2/2024 0 Comments Wwdc 2020 inviteTo learn more about using machine learning in your apps, check out the following talks.Īdditionally, we have brought dictation locally to the device for many languages. We leverage the powerful Neural Engine in our chips, allowing us to improve machine-learning models without revealing user data to Apple or any third parties. Last year, we used this technology to improve models for QuickType and Siri voice recognition from users who opt in to improve our products.Īnd this year, we're bringing new private federated learning use cases. This way, we can build centralized models on our servers without ever having access to user data. PFL works by having devices send differentially private model updates instead of sending the user data. Since iOS 13, we have been using private federated learning, or PFL, to build machine-learning models on potentially sensitive data. We've been leveraging on-device learning for many of our features. And keeping data locally automatically takes advantage of the strong security protections we have on our devices. It's as easy as dragging and dropping these models into your Xcode project. So we're creating more ways to leverage Core ML to build and train models on-device. It additionally requires extra work to secure customer data against breaches or other threats.īut sometimes you need to collect data to train a machine-learning model. So, what is the benefit of operating on data without sending it off a user's device to a remote server? When you send data to a remote server, the user loses their ability to control who can access it, who the data will in turn be shared with and what the data may be used for. Let's go through these one by one as I showcase how each influences changes that we've made this year, starting with on-device processing. These four pillars help us build strong privacy protections into our features to continue building trust with our users. And transparency and control- providing the user understanding and control over their data. Security protections, which enforce the privacy protections on our platform. Data minimization- requesting and only using data that you actually need. On-device processing- processing data locally, without sending it to a server. So, what is our approach to privacy? At Apple, we have four fundamental privacy pillars that guide the products and features that we make. I'll walk through our approach to privacy at Apple and then go through how we can apply our privacy principles to mitigate user tracking in our ecosystem. Today, we're going to take you through how we build trust with our users through better privacy. Hi, I'm Rohith, and I'm joined today by my colleague Brandon.
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