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Private rank aggregation under local differential privacy

https://doi.org/10.1002/int.22261
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What this work says

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

inferredcites →https://doi.org/10.1007/978-3-319-24753-3openalex-snapshot
A Practical Guide to Averaging Functions
inferredcites →https://doi.org/10.1016/j.jesp.2017.01.006openalex-snapshot
Beyond the Turk: Alternative platforms for crowdsourcing behavioral research
inferredcites →https://doi.org/10.1016/j.pmcj.2012.12.004openalex-snapshot
Application and analysis of multidimensional negative surveys in participatory sensing applications
inferredcites →https://doi.org/10.1017/cbo9781107446984openalex-snapshot
Handbook of Computational Social Choice
inferredcites →https://doi.org/10.1037/per0000191openalex-snapshot
Using online, crowdsourcing platforms for data collection in personality disorder research: The example of Amazon’s Mechanical Turk.
inferredcites →https://doi.org/10.1080/01621459.1965.10480775openalex-snapshot
Randomized Response: A Survey Technique for Eliminating Evasive Answer Bias
inferredcites →https://doi.org/10.1080/01621459.1969.10500991openalex-snapshot
The Unrelated Question Randomized Response Model: Theoretical Framework
inferredcites →https://doi.org/10.1080/17517575.2018.1442935openalex-snapshot
DPWeVote: differentially private weighted voting protocol for cloud-based decision-making
inferredcites →https://doi.org/10.1093/biomet/44.1-2.114openalex-snapshot
NON-NULL RANKING MODELS. I
inferredcites →https://doi.org/10.1109/focs.2008.27openalex-snapshot
What Can We Learn Privately?
inferredcites →https://doi.org/10.1109/icdcs47774.2020.00037openalex-snapshot
Towards Differentially Private Truth Discovery for Crowd Sensing Systems
inferredcites →https://doi.org/10.1109/icde.2008.4497479openalex-snapshot
OptRR: Optimizing Randomized Response Schemes for Privacy-Preserving Data Mining
inferredcites →https://doi.org/10.1109/ms.2011.67openalex-snapshot
Usenix Security Symposium
inferredcites →https://doi.org/10.1109/tifs.2016.2632069openalex-snapshot
Differential Private Data Collection and Analysis Based on Randomized Multiple Dummies for Untrusted Mobile Crowdsensing
inferredcites →https://doi.org/10.1109/tifs.2017.2663337openalex-snapshot
Individual Differential Privacy: A Utility-Preserving Formulation of Differential Privacy Guarantees
inferredcites →https://doi.org/10.1109/tifs.2017.2718487openalex-snapshot
Optimal Differentially Private Mechanisms for Randomised Response
inferredcites →https://doi.org/10.1109/tkde.2016.2535242openalex-snapshot
Crowdsourced Data Management: A Survey
inferredcites →https://doi.org/10.1109/tkde.2017.2697856openalex-snapshot
Differentially Private Data Publishing and Analysis: A Survey
inferredcites →https://doi.org/10.1137/1.9781611974973.75openalex-snapshot
Differentially Private Rank Aggregation
inferredcites →https://doi.org/10.1145/1250790.1250803openalex-snapshot
Smooth sensitivity and sampling in private data analysis
inferredcites →https://doi.org/10.1145/1411509.1411513openalex-snapshot
Aggregating inconsistent information
inferredcites →https://doi.org/10.1145/2666468openalex-snapshot
Privacy Aware Learning
inferredcites →https://doi.org/10.1145/2892555openalex-snapshot
Truthful Mechanisms for Agents That Value Privacy
inferredcites →https://doi.org/10.1145/3170427.3188511openalex-snapshot
"It was a shady HIT"
inferredcites →https://doi.org/10.1145/956750.956823openalex-snapshot
Nantonac collaborative filtering
inferredcites →https://doi.org/10.1201/b10476openalex-snapshot
Randomized Response and Indirect Questioning Techniques in Surveys
inferredcites →https://doi.org/10.14778/2809974.2809982openalex-snapshot
Rank aggregation with ties: Experiments and Analysis
inferredcites →https://doi.org/10.1561/0400000042openalex-snapshot
The Algorithmic Foundations of Differential Privacy
inferredcites →https://doi.org/10.18637/jss.v071.i12openalex-snapshot
PerMallows: AnRPackage for Mallows and Generalized Mallows Models
inferredcites →https://doi.org/10.2139/ssrn.2228728openalex-snapshot
Mechanical Turk is Not Anonymous
inferredcites →https://doi.org/10.2200/s00735ed1v01y201609spt018openalex-snapshot
Differential Privacy: From Theory to Practice
inferredcites →https://doi.org/10.3758/s13428-011-0081-0openalex-snapshot
The viability of crowdsourcing for survey research
inferredcites →https://doi.org/10.48550/arxiv.1908.04920openalex-snapshot
Aggregating Votes with Local Differential Privacy: Usefulness, Soundness vs. Indistinguishability

What points at this work

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

inferred← citeshttps://doi.org/10.1002/cpe.6680openalex-snapshot
PriTxt: A privacy risk assessment method for text data based on semantic correlation learning
inferred← citeshttps://doi.org/10.1002/int.22499openalex-snapshot
A lightweight 3D convolutional neural network for deepfake detection
inferred← citeshttps://doi.org/10.1002/int.22619openalex-snapshot
Privacy budget management and noise reusing in multichain environment
inferred← citeshttps://doi.org/10.1007/978-3-030-86137-7_22openalex-snapshot
Privacy Risk Assessment for Text Data Based on Semantic Correlation Learning
inferred← citeshttps://doi.org/10.1007/978-3-031-05933-9_19openalex-snapshot
Distributed Differentially Private Ranking Aggregation
inferred← citeshttps://doi.org/10.1007/978-3-031-46775-2_4openalex-snapshot
Differentially Private Probabilistic Social Choice in the Shuffle Model
inferred← citeshttps://doi.org/10.1007/s00778-024-00895-4openalex-snapshot
Differentially private explanations for aggregate query answers
inferred← citeshttps://doi.org/10.1080/01621459.2025.2484843openalex-snapshot
Rate-Optimal Rank Aggregation with Private Pairwise Rankings
inferred← citeshttps://doi.org/10.1080/01621459.2026.2612773openalex-snapshot
Optimal Differentially Private Ranking from Pairwise Comparisons
inferred← citeshttps://doi.org/10.1109/cacml64929.2025.11010938openalex-snapshot
Fine-grained Infectious Disease Surveillance Data Aggregation Scheme in Cloud Computing
inferred← citeshttps://doi.org/10.1109/isit54713.2023.10206447openalex-snapshot
The Privacy-Utility Tradeoff in Rank-Preserving Dataset Obfuscation
inferred← citeshttps://doi.org/10.1109/jiot.2024.3356593openalex-snapshot
PFDAM: Privacy-Preserving Fine-Grained Data Aggregation Scheme Supporting Multifunctionality in Smart Grid
inferred← citeshttps://doi.org/10.1109/tce.2026.3655419openalex-snapshot
Deep Learning-Driven Data Security Modeling in Next Generation Consumer Electronics-Powered Education Communication Systems
inferred← citeshttps://doi.org/10.1109/tcss.2022.3225096openalex-snapshot
Distributed Differentially Private Ranking Aggregation
inferred← citeshttps://doi.org/10.1109/tit.2023.3348032openalex-snapshot
Retrieving Data Permutations From Noisy Observations: Asymptotics
inferred← citeshttps://doi.org/10.1109/tkde.2022.3207486openalex-snapshot
Analyzing Preference Data With Local Privacy: Optimal Utility and Enhanced Robustness
inferred← citeshttps://doi.org/10.1145/3813822.3813914openalex-snapshot
Ranking Aggregation with Alternative Scoring under Local Differential Privacy
inferred← citeshttps://doi.org/10.1155/2020/8829523openalex-snapshot
A Comprehensive Survey on Local Differential Privacy
inferred← citeshttps://doi.org/10.14778/3561261.3561271openalex-snapshot
DPXPlain
inferred← citeshttps://doi.org/10.1609/aaai.v36i6.20544openalex-snapshot
Private Rank Aggregation in Central and Local Models
inferred← citeshttps://doi.org/10.1609/aaai.v37i5.25714openalex-snapshot
Differentially Private Condorcet Voting
inferred← citeshttps://doi.org/10.1613/jair.1.13734openalex-snapshot
Learning to Design Fair and Private Voting Rules
inferred← citeshttps://doi.org/10.24963/ijcai.2023/785openalex-snapshot
Learning to Design Fair and Private Voting Rules (Extended Abstract)
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