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The shuffle model is recently proposed to address the issue of severe utilityloss in Local Differential Privacy (LDP) due to distributed datarandomization.In the shuffle model, a shuffler is utilized to break the linkbetween the user identity and the message uploaded to the data analyst. Sinceless noise needs to be introduced to achieve the same privacy guarantee,following this paradigm, the utility of privacy-preserving data collection isimproved.  We propose DUMP (\underline{DUM}my-\underline{P}oint-based), a framework forprivacy-preserving histogram estimation in the shuffle model. The core of DUMPis a new concept of \emph{dummy blanket}, which enables enhancing privacy byjust introducing \textit{points }on the user side and further improving theutility of the shuffle model.We instantiate DUMP by proposing two protocols:pureDUMP and mixDUMP, and conduct a comprehensive experimental evaluation tocompare them with existing protocols. The experimental results show that, underthe same privacy guarantee, (1) the proposed protocols have significantimprovements in communication efficiency over all existing multi-messageprotocols, by at least 3 orders of magnitude; (2) they achieve competitiveutility, while the only known protocol (Ghazi \textit{et al.}, PMLR 2020)having better utility than ours employs hard-to-exactly-sample distributionswhich are vulnerable to floating-point attacks (CCS 2012).

Privacy Enhancement via Dummy Points in the Shuffle Model