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In this work we introduce a new sketch for this task, based on an exponentially distributed counting Bloom filter. We combine this sketch with a communication-efficient multi-party protocol to solve the task in the multi-worker setting. Our protocol exhibits both differential privacy and security guarantees in the honest-but-curious model and in the presence of large subsets of colluding workers; furthermore, its reach and frequency histogram estimates have a provably small error. Finally, we show the practicality of the protocol by evaluating it on internet-scale audiences.<\/jats:p>","DOI":"10.2478\/popets-2022-0019","type":"journal-article","created":{"date-parts":[[2021,11,21]],"date-time":"2021-11-21T02:40:56Z","timestamp":1637462456000},"page":"373-395","source":"Crossref","is-referenced-by-count":5,"title":["Multiparty Reach and Frequency Histogram: Private, Secure, and Practical"],"prefix":"10.56553","volume":"2022","author":[{"given":"Badih","family":"Ghazi","sequence":"first","affiliation":[{"name":"Google Research"}]},{"given":"Ben","family":"Kreuter","sequence":"additional","affiliation":[{"name":"Google"}]},{"given":"Ravi","family":"Kumar","sequence":"additional","affiliation":[{"name":"Google Research"}]},{"given":"Pasin","family":"Manurangsi","sequence":"additional","affiliation":[{"name":"Google Research"}]},{"given":"Jiayu","family":"Peng","sequence":"additional","affiliation":[{"name":"Google"}]},{"given":"Evgeny","family":"Skvortsov","sequence":"additional","affiliation":[{"name":"Google"}]},{"given":"Yao","family":"Wang","sequence":"additional","affiliation":[{"name":"Google"}]},{"given":"Craig","family":"Wright","sequence":"additional","affiliation":[{"name":"Google"}]}],"member":"35752","published-online":{"date-parts":[[2021,11,20]]},"reference":[{"key":"2022062314363925906_j_popets-2022-0019_ref_001","doi-asserted-by":"crossref","unstructured":"[1] J. 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