Top Highlights

• In practice, only the biggest companies in the world (Apple, Google) have access to enough data to leverage local differential privacy successfully
• The idea behind shuffling is that by taking a middle approach between local and centralized, combined with some clever math, it’s possible to maintain privacy while achieving a higher level of accuracy
• The shuffler is a separate service that is responsible for receiving, grouping, and shuffling the data
• It removes explicitly identifying features as well as metadata that could associate information with a specific user such as arrival time or IP address
• The analyzer decodes the second layer of encryption in order to access and analyze the data

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