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Decision Tree Classification with Differential Privacy
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/j.1538-7305.1948.tb00917.xopenalex-snapshot
A Mathematical Theory of Communication
inferredcites →https://doi.org/10.1007/11681878_14openalex-snapshot
Calibrating Noise to Sensitivity in Private Data Analysis
inferredcites →https://doi.org/10.1007/978-0-387-70992-5openalex-snapshot
Privacy-Preserving Data Mining: Models and Algorithms
inferredcites →https://doi.org/10.1007/978-0-387-70992-5_2openalex-snapshot
A General Survey of Privacy-Preserving Data Mining Models and Algorithms
inferredcites →https://doi.org/10.1007/978-3-319-26350-2_17openalex-snapshot
A Differentially Private Random Decision Forest Using Reliable Signal-to-Noise Ratios
inferredcites →https://doi.org/10.1007/978-3-319-91458-9_46openalex-snapshot
Qualitative Instead of Quantitative: Towards Practical Data Analysis Under Differential Privacy
inferredcites →https://doi.org/10.1007/978-3-540-44918-8openalex-snapshot
Quality Measures in Data Mining
inferredcites →https://doi.org/10.1007/978-3-642-25704-9_7openalex-snapshot
EXPLORE: A Novel Decision Tree Classification Algorithm
inferredcites →https://doi.org/10.1007/s10618-012-0295-5openalex-snapshot
Training and assessing classification rules with imbalanced data
inferredcites →https://doi.org/10.1016/j.dss.2010.12.003openalex-snapshot
An empirical evaluation of the comprehensibility of decision table, tree and rule based predictive models
inferredcites →https://doi.org/10.1016/j.eswa.2017.01.034openalex-snapshot
Differentially private random decision forests using smooth sensitivity
inferredcites →https://doi.org/10.1016/j.inffus.2010.11.004openalex-snapshot
Random feature weights for decision tree ensemble construction
inferredcites →https://doi.org/10.1016/j.jksuci.2014.06.015openalex-snapshot
An anonymization technique using intersected decision trees
inferredcites →https://doi.org/10.1016/j.knosys.2011.05.011openalex-snapshot
Privacy preserving data mining: A noise addition framework using a novel clustering technique
inferredcites →https://doi.org/10.1016/j.procs.2012.09.050openalex-snapshot
Towards A Differential Privacy and Utility Preserving Machine Learning Classifier
inferredcites →https://doi.org/10.1108/ir.1999.04926fae.001openalex-snapshot
Lecture Notes in Computer Science 1205
inferredcites →https://doi.org/10.1109/cbms.2015.54openalex-snapshot
Secure and Private Management of Healthcare Databases for Data Mining
inferredcites →https://doi.org/10.1109/csf.2014.35openalex-snapshot
Differential Privacy: An Economic Method for Choosing Epsilon
inferredcites →https://doi.org/10.1109/focs.2007.41openalex-snapshot
Towards Sharp Inapproximability For Any 2-CSP
inferredcites →https://doi.org/10.1109/icacci.2014.6968348openalex-snapshot
Differential private random forest
inferredcites →https://doi.org/10.1109/icde.2005.143openalex-snapshot
Top-Down Specialization for Information and Privacy Preservation
inferredcites →https://doi.org/10.1109/icde.2008.4497436openalex-snapshot
Privacy: Theory meets Practice on the Map
inferredcites →https://doi.org/10.1109/icdm.2003.1250902openalex-snapshot
Is random model better? On its accuracy and efficiency
inferredcites →https://doi.org/10.1109/icdm.2012.19openalex-snapshot
ConfDTree: Improving Decision Trees Using Confidence Intervals
inferredcites →https://doi.org/10.1109/icdm.2015.76openalex-snapshot
Differentially Private Random Forest with High Utility
inferredcites →https://doi.org/10.1109/icdmw.2009.52openalex-snapshot
Differential Privacy for Clinical Trial Data: Preliminary Evaluations
inferredcites →https://doi.org/10.1109/icdmw.2013.131openalex-snapshot
A Semi-Supervised Learning Approach to Differential Privacy
inferredcites →https://doi.org/10.1109/icpr.2014.618openalex-snapshot
Quality Evaluation of an Anonymized Dataset
inferredcites →https://doi.org/10.1109/msp.2013.2259911openalex-snapshot
Signal Processing and Machine Learning with Differential Privacy: Algorithms and Challenges for Continuous Data
inferredcites →https://doi.org/10.1109/tkde.2007.1015openalex-snapshot
Anonymizing Classification Data for Privacy Preservation
inferredcites →https://doi.org/10.1109/tkde.2017.2697856openalex-snapshot
Differentially Private Data Publishing and Analysis: A Survey
inferredcites →https://doi.org/10.1142/s021848850200165xopenalex-snapshot
ACHIEVING k-ANONYMITY PRIVACY PROTECTION USING GENERALIZATION AND SUPPRESSION
inferredcites →https://doi.org/10.1145/1132960.1132963openalex-snapshot
Interestingness measures for data mining
inferredcites →https://doi.org/10.1145/1250790.1250803openalex-snapshot
Smooth sensitivity and sampling in private data analysis
inferredcites →https://doi.org/10.1145/1749603.1749605openalex-snapshot
Privacy-preserving data publishing
inferredcites →https://doi.org/10.1145/1835804.1835868openalex-snapshot
Data mining with differential privacy
inferredcites →https://doi.org/10.1145/1989323.1989345openalex-snapshot
No free lunch in data privacy
inferredcites →https://doi.org/10.1145/2020408.2020487openalex-snapshot
Differentially private data release for data mining
inferredcites →https://doi.org/10.1145/2213836.2213910openalex-snapshot
Differential privacy in data publication and analysis
inferredcites →https://doi.org/10.1145/2450142.2450148openalex-snapshot
A learning theory approach to noninteractive database privacy
inferredcites →https://doi.org/10.1145/2882903.2882931openalex-snapshot
Principled Evaluation of Differentially Private Algorithms using DPBench
inferredcites →https://doi.org/10.1145/76894.76895openalex-snapshot
Security-control methods for statistical databases: a comparative study
inferredcites →https://doi.org/10.1145/775047.775131openalex-snapshot
Instability of decision tree classification algorithms
inferredcites →https://doi.org/10.1198/106186006x133933openalex-snapshot
Unbiased Recursive Partitioning: A Conditional Inference Framework
inferredcites →https://doi.org/10.1201/9781315139470openalex-snapshot
Classification And Regression Trees
inferredcites →https://doi.org/10.1214/15-aoas848openalex-snapshot
Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model
inferredcites →https://doi.org/10.1214/ss/1009213726openalex-snapshot
Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author)
inferredcites →https://doi.org/10.14778/2732269.2732271openalex-snapshot
A data- and workload-aware algorithm for range queries under differential privacy
inferredcites →https://doi.org/10.14778/2824032.2824067openalex-snapshot
Differential privacy in telco big data platform
inferredcites →https://doi.org/10.1561/9781601985415openalex-snapshot
Decision Forests: A Unified Framework for Classification, Regression, Density Estimation, Manifold Learning and Semi-Supervised Learning
inferredcites →https://doi.org/10.1561/9781601988195openalex-snapshot
The Algorithmic Foundations of Differential Privacy
inferredcites →https://doi.org/10.1613/jair.279openalex-snapshot
Improved Use of Continuous Attributes in C4.5
inferredcites →https://doi.org/10.2200/s00735ed1v01y201609spt018openalex-snapshot
Differential Privacy: From Theory to Practice
inferredcites →https://doi.org/10.48550/arxiv.1410.6973openalex-snapshot
Differentially- and non-differentially-private random decision trees
inferredcites →https://doi.org/10.48550/arxiv.1412.7584openalex-snapshot
Differential Privacy and Machine Learning: a Survey and Review
inferredcites →https://doi.org/10.5555/2612167.2612170openalex-snapshot
On Syntactic Anonymity and Differential Privacy
inferredcites →https://doi.org/10.5860/choice.49-3305openalex-snapshot
Data mining: concepts and techniques
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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A Differentially Private Random Decision Tree Classifier with High Utility
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Local Differential Privacy for Private Construction of Classification Algorithms
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A Machine Learning Framework for Detection of Fake News
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Differentially private tree-based redescription mining
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Experimental Evaluation of Spectrum Handoff Management with Machine Learning Algorithms Using Software Defined Radio
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Complexity analysis and practical resolution of the data classification problem with private characteristics
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CTI-SOC2M2 – The quest for mature, intelligence-driven security operations and incident response capabilities
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Local differential privacy and its applications: A comprehensive survey
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Differentially private 1R classification algorithm using artificial bee colony and differential evolution
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Partial Discharge Diagnosis in GIS based on Pulse Sequence Features and Optimized Machine Learning Classification Techniques
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Accuracy, Interpretability, and Differential Privacy via Explainable Boosting
inferred← citeshttps://doi.org/10.5753/sbsi.2025.245970openalex-snapshot
Machine learning approaches for efficient recognition of Brazilian Sign Language
inferred← citeshttps://doi.org/10.70675/9719f9b2za9f9z4e0fza756ze63dcf2f6b32openalex-snapshot
Addressing interpretability fairness & privacy in machine learning through combinatorial optimization methods
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