Privacy-preserving tabular data publishing: A comprehensive evaluation from web to cloud

Abdelhameed, Saad A.; Moussa, Sherin M.; Khalifa, Mohamed

doi:10.1016/j.cose.2017.09.002

Cited by 26 publications

(10 citation statements)

References 32 publications

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“…Khan et al [23] introduced the θ-sensitive k-anonymity privacy model, in which the threshold θ determines the diversity level of an equivalence class, to prevent the sensitive variance attack when publishing electronic health records. A comprehensive survey of privacy-preserving data publishing can be found in [24][25][26][27][28][29].…”

Section: Related Workmentioning

confidence: 99%

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Efficiently Supporting Online Privacy-Preserving Data Publishing in a Distributed Computing Environment

Kim

2021

Applied Sciences

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There has recently been an increasing need for the collection and sharing of microdata containing information regarding an individual entity. Because microdata typically contain sensitive information on an individual, releasing it directly for public use may violate existing privacy requirements. Thus, extensive studies have been conducted on privacy-preserving data publishing (PPDP), which ensures that any microdata released satisfy the privacy policy requirements. Most existing privacy-preserving data publishing algorithms consider a scenario in which a data publisher, receiving a request for the release of data containing personal information, anonymizes the data prior to publishing—a process that is usually conducted offline. However, with the increasing demand for the sharing of data among various parties, it is more desirable to integrate the data anonymization functionality into existing systems that are capable of supporting online query processing. Thus, we developed a novel scheme that is able to efficiently anonymize the query results on the fly, and thus support efficient online privacy-preserving data publishing. In particular, given a user’s query, the proposed approach effectively estimates the generalization level of each quasi-identifier attribute, thereby achieving the k-anonymity property in the query result datasets based on the statistical information without applying k-anonymity on all actual datasets, which is a costly procedure. The experiment results show that, through the proposed method, significant gains in processing time can be achieved.

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Section: Related Workmentioning

confidence: 99%

“…EQ n 5 = {(10-20, 3060-3070), (10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(3071)(3072)(3073)(3074)(3075)(3076)(3077)(3078)(3079)(3080), (21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(3060)(3061)(3062)(3063)(3064)(3065)(3066)(3067)(3068)(3069)(3070), (21-30, 3071-3087)}.…”

Section: Phase I: Estimating the Generalization Levelunclassified

Efficiently Supporting Online Privacy-Preserving Data Publishing in a Distributed Computing Environment

Kim

2021

Applied Sciences

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“…Therefore the privacy preservation of data streams has attracted increasing attention from industry and academia [4][5][6][7] . Anonymization is a commonly used privacy-preserving method that allows the sharing of sensitive data with a guarantee that the individuals, i.e., the subjects of the data, cannot be recognized [8] . Anonymization is mainly achieved through two techniques: generalization and suppression.…”

Section: Introductionmentioning

confidence: 99%

IDEA: A utility-enhanced approach to incomplete data stream anonymization

Lei¹,

Chen²,

Luo

et al. 2022

Tsinghua Sci. Technol.

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The prevalence of missing values in the data streams collected in real environments makes them impossible to ignore in the privacy preservation of data streams. However, the development of most privacy preservation methods does not consider missing values. A few researches allow them to participate in data anonymization but introduce extra considerable information loss. To balance the utility and privacy preservation of incomplete data streams, we present a utility-enhanced approach for Incomplete Data strEam Anonymization (IDEA). In this approach, a slide-window-based processing framework is introduced to anonymize data streams continuously, in which each tuple can be output with clustering or anonymized clusters. We consider the dimensions of attribute and tuple as the similarity measurement, which enables the clustering between incomplete records and complete records and generates the cluster with minimal information loss. To avoid the missing value pollution, we propose a generalization method that is based on maybe match for generalizing incomplete data. The experiments conducted on real datasets show that the proposed approach can efficiently anonymize incomplete data streams while effectively preserving utility.

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“…Three kinds of the disclosure can cause privacy leakage, identity disclosure, attribute disclosure, and membership disclosure [5]. In attribute disclosure and identity disclosure, the intruder identifies that the tuple of the target individual is found in the released dataset and he aims to acquire some private/sensitive data about that individual from the released dataset [6]. Serious issues that lead to identity disclosure are quasi-identifier (QID) value linking and the attacker's knowledge background.…”

Section: Introductionmentioning

confidence: 99%

Quasi-Identifier Recognition Algorithm for Privacy Preservation of Cloud Data Based on Risk Reidentification

Mansour

Siraj

Ghaleb

et al. 2021

Wireless Communications and Mobile Computing

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Cloud computing plays an essential role as a source for outsourcing data to perform mining operations or other data processing, especially for data owners who do not have sufficient resources or experience to execute data mining techniques. However, the privacy of outsourced data is a serious concern. Most data owners are using anonymization-based techniques to prevent identity and attribute disclosures to avoid privacy leakage before outsourced data for mining over the cloud. In addition, data collection and dissemination in a resource-limited network such as sensor cloud require efficient methods to reduce privacy leakage. The main issue that caused identity disclosure is quasi-identifier (QID) linking. But most researchers of anonymization methods ignore the identification of proper QIDs. This reduces the validity of the used anonymization methods and may thus lead to a failure of the anonymity process. This paper introduces a new quasi-identifier recognition algorithm that reduces identity disclosure which resulted from QID linking. The proposed algorithm is comprised of two main stages: (1) attribute classification (or QID recognition) and (2) QID dimension identification. The algorithm works based on the reidentification of risk rate for all attributes and the dimension of QIDs where it determines the proper QIDs and their suitable dimensions. The proposed algorithm was tested on a real dataset. The results demonstrated that the proposed algorithm significantly reduces privacy leakage and maintains the data utility compared to recent related algorithms.

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Privacy-preserving tabular data publishing: A comprehensive evaluation from web to cloud

Cited by 26 publications

References 32 publications

Efficiently Supporting Online Privacy-Preserving Data Publishing in a Distributed Computing Environment

Efficiently Supporting Online Privacy-Preserving Data Publishing in a Distributed Computing Environment

IDEA: A utility-enhanced approach to incomplete data stream anonymization

Quasi-Identifier Recognition Algorithm for Privacy Preservation of Cloud Data Based on Risk Reidentification

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