Privacy-Preserving Techniques for Data Analysis and Machine Learning leaf node


URI

https://openalex.org/T10764

Label

Privacy-Preserving Techniques for Data Analysis and Machine Learning

Description

This cluster of papers focuses on privacy-preserving techniques for data analysis and machine learning, including topics such as differential privacy, federated learning, k-anonymity, secure computation, and location privacy. The papers explore methods to protect sensitive information while performing data mining, machine learning, and statistical analysis.

Implementation

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@prefix owl: <http://www.w3.org/2002/07/owl#> .
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .
@prefix skos: <http://www.w3.org/2004/02/skos/core#> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .

<https://openalex.org/T10764> a skos:Concept ;
    rdfs:label "Privacy-Preserving Techniques for Data Analysis and Machine Learning"@en ;
    rdfs:isDefinedBy openalex: ;
    owl:sameAs <https://en.wikipedia.org/wiki/Differential_privacy>,
        <https://openalex.org/T10764> ;
    skos:broader oasubfields:1702 ;
    skos:definition "This cluster of papers focuses on privacy-preserving techniques for data analysis and machine learning, including topics such as differential privacy, federated learning, k-anonymity, secure computation, and location privacy. The papers explore methods to protect sensitive information while performing data mining, machine learning, and statistical analysis."@en ;
    skos:inScheme openalex: ;
    skos:prefLabel "Privacy-Preserving Techniques for Data Analysis and Machine Learning"@en ;
    openalex:cited_by_count 723855 ;
    openalex:works_count 50939 .