Privacy Preserving Techniques for Big Data: A Survey

Patel, Kajol; Jethava, Gordhan

doi:10.1109/icicct.2018.8473289

Cited by 6 publications

(3 citation statements)

References 11 publications

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“…In the basic privacy models (such as k-anonymity [7-9, 11-13, 18, 28], l-diversity [40,42], and t-closeness [34,43]), the attributes of a dataset were categorized into two groups: sensitive and nonsensitive. Meanwhile, most of the recent researchers such as in [9,[44][45][46][47] divide the dataset attributes into three types: QID, SA, and NS (not including identifiers) directly. Accordingly, the classification of dataset attributes in this study is divided into three types of QID, SA, and NS (not including identifiers) utilizing the same definitional meaning of each category as in the previous work in [9,[44][45][46][47].…”

Section: The Proposed Qid Recognition Algorithmmentioning

confidence: 99%

“…Meanwhile, most of the recent researchers such as in [9,[44][45][46][47] divide the dataset attributes into three types: QID, SA, and NS (not including identifiers) directly. Accordingly, the classification of dataset attributes in this study is divided into three types of QID, SA, and NS (not including identifiers) utilizing the same definitional meaning of each category as in the previous work in [9,[44][45][46][47].…”

Section: The Proposed Qid Recognition Algorithmmentioning

confidence: 99%

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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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Section: The Proposed Qid Recognition Algorithmmentioning

confidence: 99%

Section: The Proposed Qid Recognition Algorithmmentioning

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

View full text Add to dashboard Cite

show abstract

“…In general, de-identification of personal information is a critical issue in big data ecosystems [1][2][3]. In order to enhance privacy, de-identification technologies are required to reduce the threat of re-identification [4], about which there is active discussion specifically surrounding single output, linkability, inference, and indistinguishability [5].…”

Section: Introductionmentioning

confidence: 99%

Security of IoT Device: Perspective Forensic/Anti-Forensic Issues on Invalid Area of NAND Flash Memory

Ahn

Lee

2022

IEEE Access

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NAND flash memory-based IoT device can potentially still leave behind original personal data in an invalid area even if the data has been deleted. In this paper, we raise the forensic issue of original data remaining in unmanaged blocks caused by NAND flash memory and introduce methods for secure deletion of such data in the invalid area. We also propose a verification technique for secure deletion that is performed based on cell count information, which refers to the difference in bits between personal data and data stored in the block. The pass/fail of the verification technique according to the cell count information is determined in consideration of error correction capabilities. With the forensic issue of de-identification being a vital theme in the big data industry, the threat of serious privacy breaches coupled with our proposal to prevent these attacks will prove to be critical technological necessities in the future.

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Enhanced anonymous models for microdata release based on sensitive levels partition

Song

Wang

Sun

et al. 2020

Computer Communications

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Privacy Preserving Techniques for Big Data: A Survey

Cited by 6 publications

References 11 publications

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

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

Security of IoT Device: Perspective Forensic/Anti-Forensic Issues on Invalid Area of NAND Flash Memory

Enhanced anonymous models for microdata release based on sensitive levels partition

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