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Distributed Cardinality Estimation of Set Operations with Differential Privacy

https://doi.org/10.1109/pac.2017.43

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Calibrating Noise to Sensitivity in Private Data Analysis

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O-PSI: Delegated Private Set Intersection on Outsourced Datasets

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Privately Computing Set-Union and Set-Intersection Cardinality via Bloom Filters

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Sanitization of Call Detail Records via Differentially-Private Bloom Filters

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Efficient Private Matching and Set Intersection

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Differential Privacy: A Survey of Results

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Differentially Private Continual Monitoring of Heavy Hitters from Distributed Streams

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BLIP: Non-interactive Differentially-Private Similarity Computation on Bloom filters

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”Better Than Nothing” Privacy with Bloom Filters: To What Extent?

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Fast and Private Computation of Cardinality of Set Intersection and Union

inferredcites →https://doi.org/10.1007/978-3-662-43936-4_26openalex-snapshot

A Scalable and Efficient Privacy Preserving Global Itemset Support Approximation Using Bloom Filters

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Cardinality estimation and dynamic length adaptation for Bloom filters

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Randomized Response: A Survey Technique for Eliminating Evasive Answer Bias

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Network Applications of Bloom Filters: A Survey

inferredcites →https://doi.org/10.1109/allerton.2013.6736718openalex-snapshot

Local privacy and statistical minimax rates

inferredcites →https://doi.org/10.1109/focs.2007.41openalex-snapshot

Towards Sharp Inapproximability For Any 2-CSP

inferredcites →https://doi.org/10.1109/focs.2007.66openalex-snapshot

Mechanism Design via Differential Privacy

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What Can We Learn Privately?

inferredcites →https://doi.org/10.1109/focs.2010.14openalex-snapshot

The Limits of Two-Party Differential Privacy

inferredcites →https://doi.org/10.1109/focs.2013.53openalex-snapshot

Local Privacy and Statistical Minimax Rates

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Protocols for secure computations

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Protocols for secure computations

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Practical Data-Swapping: The First Steps

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Robust De-anonymization of Large Sparse Datasets

inferredcites →https://doi.org/10.1111/j.1748-720x.1997.tb01885.xopenalex-snapshot

Weaving Technology and Policy Together to Maintain Confidentiality

inferredcites →https://doi.org/10.1145/1806689.1806786openalex-snapshot

On the geometry of differential privacy

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Pan-private algorithms via statistics on sketches

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PDA

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Privately releasing conjunctions and the statistical query barrier

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Cloud-enabled privacy-preserving collaborative learning for mobile sensing

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RAPPOR

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Differentially private data aggregation with optimal utility

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Local, Private, Efficient Protocols for Succinct Histograms

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Practical Bluetooth Traffic Sniffing

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Heavy Hitter Estimation over Set-Valued Data with Local Differential Privacy

inferredcites →https://doi.org/10.1145/348.349openalex-snapshot

Practical data-swapping: the first steps

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Space/time trade-offs in hash coding with allowable errors

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A data distortion by probability distribution

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Security-control methods for statistical databases: a comparative study

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Revealing information while preserving privacy

inferredcites →https://doi.org/10.1201/b14880openalex-snapshot

Probabilistic Models for Dynamical Systems

inferredcites →https://doi.org/10.1504/ijkesdp.2009.021985openalex-snapshot

Similarity measures for binary and numerical data: a survey

inferredcites →https://doi.org/10.1515/popets-2016-0015openalex-snapshot

Building a RAPPOR with the Unknown: Privacy-Preserving Learning of Associations and Data Dictionaries

inferredcites →https://doi.org/10.29012/jpc.v5i1.625openalex-snapshot

Privacy via the Johnson-Lindenstrauss Transform

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Travel Time Forecasting and Dynamic Origin-Destination Estimation for Freeways Based on Bluetooth Traffic Monitoring

inferredcites →https://doi.org/10.3233/jcs-2005-13401openalex-snapshot

Secure set intersection cardinality with application to association rule mining

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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RRTxFM: Probabilistic Counting for Differentially Private Statistics

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Streaming Data Linkage and Analytics

inferred← citeshttps://doi.org/10.1007/978-981-95-8447-5_1openalex-snapshot

SlideDP: A Time-Aware and Private Sketch for Cardinality Estimation over Sliding Windows

inferred← citeshttps://doi.org/10.1007/s10618-021-00778-0openalex-snapshot

Differentially Private Distance Learning in Categorical Data

inferred← citeshttps://doi.org/10.1109/jiot.2019.2916349openalex-snapshot

Privacy-Preserving Estimation of $k$ -Persistent Traffic in Vehicular Cyber-Physical Systems

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Dependability of the K Minimum Values Sketch: Protection and Comparative Analysis

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Local Differential Privately Anonymizing Online Social Networks Under HRG-Based Model

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Efficient Noise Generation Protocols for Differentially Private Multiparty Computation

inferred← citeshttps://doi.org/10.1109/tdsc.2026.3687031openalex-snapshot

Flexible Privacy-Preserving Cardinality Query for Massive Distributed Datasets

inferred← citeshttps://doi.org/10.1109/tifs.2022.3207911openalex-snapshot

Differentially Private Set Intersection for Asymmetrical ID Alignment

inferred← citeshttps://doi.org/10.1109/tkde.2022.3198478openalex-snapshot

Local Differentially Private Fuzzy Counting in Stream Data Using Probabilistic Data Structures

inferred← citeshttps://doi.org/10.1109/tmc.2018.2863296openalex-snapshot

Persistent Traffic Measurement through Vehicle-to-Infrastructure Communications in Cyber-Physical Road Systems

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DPLCF

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An Effective and Differentially Private Protocol for Secure Distributed Cardinality Estimation

inferred← citeshttps://doi.org/10.1145/3639281openalex-snapshot

An LDP Compatible Sketch for Securely Approximating Set Intersection Cardinalities

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A Fast, Mergeable, and LDP Compatible Sketch for Counting the Number of Distinct Values in Fully Dynamic Tables

inferred← citeshttps://doi.org/10.1155/2022/1258482openalex-snapshot

Network Host Cardinality Estimation Based on Artificial Neural Network

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Survey of recommender systems based on federated learning

inferred← citeshttps://doi.org/10.14778/3583140.3583146openalex-snapshot

Differentially Private Vertical Federated Clustering

inferred← citeshttps://doi.org/10.48550/arxiv.2001.11932openalex-snapshot

Efficient Differentially Private $F_0$ Linear Sketching

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On the Privacy of Sublinear-Communication Jaccard Index Estimation via Min-hash

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