Knowledge Based Access Control a model for security and privacy in the Big Data

Haourani, Lamia El; Elkalam, Anas Abou; Ouahman, Abdelah Ait

doi:10.1145/3286606.3286793

Cited by 6 publications

(4 citation statements)

References 17 publications

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“…Pseudonymization replaces an identifier with a randomly generated identifier called pseudonym and it generates several identification keys in order to create a connection between distinct information related to individuals [13]. In the following, the most used pseudonymization techniques as mentioned in [14].…”

Section: Pseudonymizationmentioning

confidence: 99%

A Classification of non-Cryptographic Anonymization Techniques Ensuring Privacy in Big Data

Ouazzani

Bakkali

2022

Int. j. commun. netw. inf. secur.

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Recently, Big Data processing becomes crucial to most enterprise and government applications due to the fast growth of the collected data. However, this data often includes private personal information that arise new security and privacy concerns. Moreover, it is widely agreed that the sheer scale of big data makes many privacy preserving techniques unavailing. Therefore, in order to ensure privacy in big data, anonymization is suggested as one of the most efficient approaches. In this paper, we will provide a new detailed classification of the most used non-cryptographic anonymization techniques related to big data including generalization and randomization approaches. Besides, the paper evaluates the presented techniques through integrity, confidentiality and credibility criteria. In addition, three relevant anonymization techniques including k-anonymity, l-diversity and t-closeness are tested on an extract of a huge real data set.

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Section: Pseudonymizationmentioning

confidence: 99%

A Classification of non-Cryptographic Anonymization Techniques Ensuring Privacy in Big Data

Ouazzani

Bakkali

2022

Int. j. commun. netw. inf. secur.

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“…e first one represents a set of information that contains personal details other than identifiers [19,20]. In addition, QI attributes like "Age" and "Gender" attributes do not identify a record directly and are separate by themselves [21,22]. Even though the individual identifiers are removed, QI attributes can be utilized by a malicious person to track an individual and to reveal its identity when we gather these attributes together with the assistance of other pieces of background knowledge information [23,24].…”

Section: Background and Definitionsmentioning

confidence: 99%

“…e second type is considered as private personal attributes [22,25]. For instance, in a hospital data set, "Disease" is a sensitive attribute; in a financial data set, "CCV" number is a sensitive attribute, and the "annual income" is a sensitive attribute in a census data set [21,23]. Moreover, sensitive attributes (SA) should be hidden while publishing and sharing data [21,24].…”

Section: Background and Definitionsmentioning

confidence: 99%

“…For instance, in a hospital data set, "Disease" is a sensitive attribute; in a financial data set, "CCV" number is a sensitive attribute, and the "annual income" is a sensitive attribute in a census data set [21,23]. Moreover, sensitive attributes (SA) should be hidden while publishing and sharing data [21,24]. Sensitive attributes could be exploited as well by third parties like data analysts, marketers, or even social media.…”

Section: Background and Definitionsmentioning

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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