Randomize the Future: Asymptotically Optimal Locally Private Frequency Estimation Protocol for Longitudinal Data

  title={Randomize the Future: Asymptotically Optimal Locally Private Frequency Estimation Protocol for Longitudinal Data},
  author={Olga Ohrimenko and Anthony Wirth and Hao Wu},
  journal={Proceedings of the 41st ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems},
  • O. Ohrimenko, A. Wirth, Hao Wu
  • Published 22 December 2021
  • Computer Science, Mathematics
  • Proceedings of the 41st ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems
Longitudinal data tracking under Local Differential Privacy (LDP) is a challenging task. Baseline solutions that repeatedly invoke a protocol designed for one-time computation lead to linear decay in the privacy or utility guarantee with respect to the number of computations. To avoid this, the recent approach of Erlingsson et al. (2020) exploits the potential sparsity of user data that changes only infrequently. Their protocol targets the fundamental problem of frequency estimation for… 

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