![]() ![]() ![]() Understanding the balance among these factors leads us to a successful practical deployment using local differential privacy. We design efficient and scalable local differentially private algorithms and provide rigorous analyses to demonstrate the tradeoffs among utility, privacy, server computation, and device bandwidth. We develop a system architecture that enables learning at scale by leveraging local differential privacy, combined with existing privacy best practices. However, accessing the data that provides such insights - for example, what users type on their keyboards and the websites they visit - can compromise user privacy. Understanding how people use their devices often helps in improving the user experience. ![]()
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