P-sensitive k-anonymity
WebJan 1, 2024 · The p-sensitive k-anonymity (Campan et al., 2010) model prevents identity disclosure but not attribute disclosure. It is computationally expensive for the adversary to compute the exact value of sensitive attribute but using similarity attacks, it is easy to distinguish the individual's categorized position. ... WebOSNs contain sensitive information about each end user that intruders may try to leak for commercial or non-commercial purposes. Therefore, ensuring different levels of privacy is a vital requirement for OSNs. ... node, and user attributes in the OSN graph. This clustering approach proposes to ensure k-anonymity, l-diversity, and t-closeness in ...
P-sensitive k-anonymity
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WebGeneralization is an important technique for protecting privacy in data dissemination. In the framework of generalization, @?-diversity is a strong notion of privacy. However, since existing @?-div... Webapproach is the 𝑘-anonymity model. Recently, several authors have recognized that 𝑘-anonymity cannot prevent attribute disclosure. To address this privacy threat, one solution would be to employ 𝑝-sensitive 𝑘-anonymity, a novel paradigm in relational data privacy, which prevents sensitive attribute
WebThe k‐anonymity property for a MM is satisfied if every QI‐cluster from MM contains k or more tuples. Definition 3 (P‐Sensitive K‐Anonymity Property). A MM satisfies the p‐ … WebIn this paper, we present an overview of p-sensitive k-anonymity models including the basic model, the extended p-sensitive k-anonymity, the constrained p-sensitive k …
Webdividuals in all situations [16]. Two similar models called p-sensitive k-anonymity [16] and l-diversity [11] were proposed in the literature in order to deal with the problems of the k … WebAug 8, 2008 · els such as p-sensitive k-anonymity [15], l-divers ity [7], (α, k)-anon ymity [19] and t-closenes s [6] were proposed. in the literature in order to d eal with the pr o …
Webk-anonymity is a property possessed by certain anonymized data.The term k-anonymity was first introduced by Pierangela Samarati and Latanya Sweeney in a paper published in 1998, although the concept dates to a 1986 paper by Tore Dalenius.. K-anonymity is an attempt to solve the problem: "Given person-specific field-structured data, produce a …
WebApr 16, 2024 · Biomedical data can be de-identified via micro-aggregation achieving k-anonymity privacy. However, the existing micro-aggregation algorithms result in low similarity within the equivalence classes ... burn and buildWebApr 14, 2024 · In fact, k-anonymization for sensitive health data is one of its most common use cases. The k in k-anonymity refers to a variable — think of the classic ‘x’ in your … halton march break 2023Webk-anonymity model have been fixed by new privacy models such as p-sensitive k-anonymity, l-diversity, ( α, k)-anonymity, and t-closeness. In this paper we introduce … burn and build gymWebJun 14, 2024 · Balanced p+-sensitive k-anonymity was proposed by as improvement, because p-sensitive p-anonymity, (p, \(\alpha \))-sensitive k-anonymity and p+-sensitive k-anonymity do not provide adequate privacy protection to the end-users. One more anonymization method, called the utility-preserving model uses h-ceiling to prevent … halton machine learningWebThe p-sensitive k-anonymity model and its enhancement, extended p-sensitive k-anonymity, are described, their properties are presented, and two diversity measures … halton machiningWebDec 23, 2016 · Existing anonymity algorithms are only anonymous quasi-identifier to achieve privacy protection, but ignore the sensitive properties of the personalized … halton mark anthony powellWebOct 23, 2009 · To address this privacy threat, one solution would be to employ p-sensitive k-anonymity, a novel paradigm in relational data privacy, which prevents sensitive … halton march break 2022