2021/06/15 by Hao Wu, Wu, Hao, Anthony Wirth +1 · 2 citations
Computer Science · #Complexity and Algorithms in Graphs #Cryptography and Data Security #Cryptography and Security (cs.CR) #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #Privacy-Preserving Technologies in Data
paper · pdf · doi:10.48550/arxiv.2106.07815
openalex publication_date 2021/06/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We present two new local differentially private algorithms for frequency estimation. One solves the fundamental frequency oracle problem; the other solves the well-known heavy hitters identification problem. Consistent with prior art, these are randomized algorithms. As a function of failure probability~β, the former achieves optimal worst-case estimation error for every~β, while the latter is optimal when~β is at least inverse polynomial in~n, the number of users. In both algorithms, server running time is~O(n) while user running time is~O(1). Our frequency-oracle algorithm achieves lower estimation error than the prior works of Bassily et al. (NeurIPS 2017). On the other hand, our heavy hitters identification method is as easily implementable as as TreeHist (Bassily et al., 2017) and has superior worst-case error, by a factor of Ω(√(log n)).