KSKV: Key-Strategy for Key-Value Data Collection with Local Differential Privacy
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Le résumé fourni par la source
In recent years, the research field of data collection under local differential privacy (LDP) has expanded its focus from elementary data types to include more complex structural data, such as set-value and graph data. However, our comprehensive review of existing literature reveals that there needs to be more studies that engage with key-value data collection. Such studies would simultaneously collect the frequencies of keys and the mean of values associated with each key. Additionally, the allocation of the privacy budget between the frequencies of keys and the means of values for each key does not yield an optimal utility tradeoff. Recognizing the importance of obtaining accurate key frequencies and mean estimations for key-value data collection, this paper presents a novel framework: the Key-Strategy Framework for Key-Value Data Collection under LDP. Initially, the Key-Strategy Unary Encoding (KS-UE) strategy is proposed within non-interactive frameworks for the purpose of privacy budget allocation to achieve precise key frequencies; subsequently, the Key-Strategy Generalized Randomized Response (KS-GRR) strategy is introduced for interactive frameworks to enhance the efficiency of collecting frequent keys through group-and-iteration methods. Both strategies are adapted for scenarios in which users possess either a single or multiple key-value pairs. Theoretically, we demonstrate that the variance of KS-UE is lower than that of existing methods. These claims are substantiated through extensive experimental evaluation on real-world datasets, confirming the effectiveness and efficiency of the KS-UE and KS-GRR strategies.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- KSKV: Key-Strategy for Key-Value Data Collection with Local Differential Privacy
- Date Crossref
- 01/01/2024
- Éditeur
- Tech Science Press
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
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Institute of Scientific and Technical Information of China pays non établi dans la noticeOrganisme public
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Beijing Forestry University pays non établi dans la noticeUniversité ou école supérieure
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University of Electronic Science and Technology of China pays non établi dans la noticeUniversité ou école supérieure
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Institute of Computing Technology pays non établi dans la noticeStructure de recherche
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China Academy of Railway Sciences pays non établi dans la noticeStructure de recherche
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China 3 School of Information Science and Technology Artificial Intelligence Development Research Center pays non établi dans la noticeUniversité ou école supérieure
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Artificial Intelligence Development Research Center pays non établi dans la noticeStructure de recherche
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NSFOCUS Inc. Industry Development Department pays non établi dans la noticeEntreprise
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School of Computer Science and Engineering pays non établi dans la noticeUniversité ou école supérieure
Institute of Scientific and Technical Information of China, Beijing Forestry University et University of Electronic Science and Technology of China, avec 6 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.