SplitX: high-performance private analytics

  title={SplitX: high-performance private analytics},
  author={Ruichuan Chen and Istemi Ekin Akkus and Paul Francis},
There is a growing body of research on mechanisms for preserving online user privacy while still allowing aggregate queries over private user data. A common approach is to store user data at users' devices, and to query the data in such a way that a differentially private noisy result is produced without exposing individual user data to any system component. A particular challenge is to design a system that scales well while limiting how much the malicious users can distort the result. This… CONTINUE READING
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