Large technology companies rely on collecting data from their users to understand their interests, and better customize the company's products. Increasingly, this must be done while preserving individual users' privacy. Recently, techniques based on randomization and data sketching have been adopted to provide data collection protocols which optimize the privacy-accuracy tradeoff. In this talk, I'll discuss methods deployed by Google and Apple to collect frequency information, and our recent work to capturing information on marginal and cumulative distributions.
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