Cardinality Estimators do not Preserve Privacy

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Cardinality Estimators do not Preserve Privacy

17 August 2018

Damien Desfontaines

Andreas Lochbihler

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Papers citing "Cardinality Estimators do not Preserve Privacy"

9 / 9 papers shown

Title
Counting Distinct Elements in the Turnstile Model with Differential Privacy under Continual Observation Palak Jain Iden Kalemaj Sofya Raskhodnikova Satchit Sivakumar Adam D. Smith 94 14 0 11 Jun 2023
Sketch-Flip-Merge: Mergeable Sketches for Private Distinct Counting J. Hehir Daniel Ting Graham Cormode FedML 32 11 0 04 Feb 2023
Privacy-Preserving Record Linkage for Cardinality Counting Nan Wu Dinusha Vatsalan M. Kâafar Sanat Ramesh 42 4 0 09 Jan 2023
Adversarial Correctness and Privacy for Probabilistic Data Structures M. Filic Kenneth G. Paterson Anupama Unnikrishnan Fernando Virdia 52 11 0 14 Sep 2022
Differentially Private Vertical Federated Clustering Zitao Li Tianhao Wang Ninghui Li FedML 94 19 0 02 Aug 2022
HyperLogLog (HLL) Security: Inflating Cardinality Estimates Pedro Reviriego Pablo Adell Daniel Ting 16 2 0 20 Nov 2020
On Distributed Differential Privacy and Counting Distinct Elements Lijie Chen Badih Ghazi Ravi Kumar Pasin Manurangsi FedML 103 31 0 21 Sep 2020
Probabilistic Counters for Privacy Preserving Data Aggregation Dominik Bojko Krzysztof Grining M. Klonowski 10 0 0 25 Mar 2020
Security of HyperLogLog (HLL) Cardinality Estimation: Vulnerabilities and Protection Pedro Reviriego Daniel Ting 18 23 0 15 Feb 2020