Research Issue in Data Anonymization in Electronic Health Service: A Survey

Gebrehiwot, Amanuel; Pawar, Ambika

doi:10.1007/978-981-10-7641-1_12

Cited by 1 publication

(2 citation statements)

References 17 publications

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“…However, this technique is vulnerable to a similarity attack [2,31,32]. A similarity attack occurs when values within an equivalence class are the same or even when there is a semantic significance between distinct sensitive values in the same equivalence class [23,34]. erefore, an adversary can deduce the different possibilities of sensitive values related to a specific individual [35].…”

Section: L-diversity Techniquementioning

confidence: 99%

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Proximity Measurement for Hierarchical Categorical Attributes in Big Data

Ouazzani

Braeken

Bakkali

2021

Security and Communication Networks

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Nearly most of the organizations store massive amounts of data in large databases for research, statistics, and mining purposes. In most cases, much of the accumulated data contain sensitive information belonging to individuals which may breach privacy. Hence, ensuring privacy in big data is considered a very important issue. The concept of privacy aims to protect sensitive information from various attacks that may violate the identity of individuals. Anonymization techniques are considered the best way to ensure privacy in big data. Various works have been already realized, taking into account horizontal clustering. The L-diversity technique is one of those techniques dealing with sensitive numerical and categorical attributes. However, the majority of anonymization techniques using L-diversity principle for hierarchical data cannot resist the similarity attack and therefore cannot ensure privacy carefully. In order to prevent the similarity attack while preserving data utility, a hybrid technique dealing with categorical attributes is proposed in this paper. Furthermore, we highlighted all the steps of our proposed algorithm with detailed comments. Moreover, the algorithm is implemented and evaluated according to a well-known information loss-based criterion which is Normalized Certainty Penalty (NCP). The obtained results show a good balance between privacy and data utility.

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Section: L-diversity Techniquementioning

confidence: 99%

“…Security and Communication Networks values in the same equivalence class [23,34]. erefore, an adversary can deduce the different possibilities of sensitive values related to a specific individual [35].…”

Section: Similarity Attackmentioning

confidence: 99%