Preserving privacy for sensitive values of individuals in data publishing based on a new additive noise approach

Zhu, Hong; Tian, Sijia; Xie, Meiyi; Yang, Mengyuan

doi:10.1109/icccn.2014.6911855

Cited by 9 publications

(5 citation statements)

References 18 publications

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“…The (α,l)model has less running time and utility loss [30]. An addictive noise approach was proposed by satisfying the conditions of l-diversity [31]. A privacy-preserving data publishing method known as MNSACM was proposed to handle numerical attributes.…”

Section: B 1:1 Micro Data With Multiple Sensitive Attributementioning

confidence: 99%

Heap Bucketization Anonymity—An Efficient Privacy-Preserving Data Publishing Model for Multiple Sensitive Attributes

et al. 2022

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The publication of a patient's dataset is essential for various medical investigations and decision-making. Currently, significant focus has been established to protect privacy during data publishing. The existing privacy models for multiple sensitive attributes do not concentrate on the correlation among the attributes, which in turn leads to much utility loss. An efficient model Heap Bucketization-anonymity (HBA) has been proposed to balance privacy and utility with multiple sensitive attributes. The Heap Bucketization-anonymity model used anatomization to vertically partition the dataset into 1. Quasi-identifier table and 2. Sensitive attribute table. The quasi-identifier is anonymized by implementing k-anonymity and slicing and the sensitive attributes are anonymized by applying slicing and Heap Bucketization. The metrics Normalized Certainty Penalty and KL-divergence have been used to compute the utility loss in the patient dataset. The experimental results show that the HB-anonymity can significantly achieve high privacy with less utility loss than other existing models. The HBanonymity model not only balances the utility and privacy also eradicates the i) background knowledge attack, ii) quasiidentifier attack iii) membership attack, iv) non-membership attack and v) fingerprint correlation attack.

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Section: B 1:1 Micro Data With Multiple Sensitive Attributementioning

confidence: 99%

Heap Bucketization Anonymity—An Efficient Privacy-Preserving Data Publishing Model for Multiple Sensitive Attributes

et al. 2022

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“…Ref. [ 32 ] proposes a (p+)-sensitive and t-closeness model for MSAs that meets t-closeness requirements for the published table.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Logical operations on fuzzy sets: The fuzzy set theory comprises the operations union, intersection, complement, and inclusion, just like classical set theory. In fuzzy logic, the various logical operations for compound statements in Equation ( 1 ) are considered as implications [ 12 , 32 ]. The implication used in the proposed approach is UNION or AND, which is defined as follows in terms of characteristic functions.…”

Section: Preliminariesmentioning

confidence: 99%

F-Classify: Fuzzy Rule Based Classification Method for Privacy Preservation of Multiple Sensitive Attributes

Attaullah

Anjum

Kanwal

et al. 2021

Sensors

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With the advent of smart health, smart cities, and smart grids, the amount of data has grown swiftly. When the collected data is published for valuable information mining, privacy turns out to be a key matter due to the presence of sensitive information. Such sensitive information comprises either a single sensitive attribute (an individual has only one sensitive attribute) or multiple sensitive attributes (an individual can have multiple sensitive attributes). Anonymization of data sets with multiple sensitive attributes presents some unique problems due to the correlation among these attributes. Artificial intelligence techniques can help the data publishers in anonymizing such data. To the best of our knowledge, no fuzzy logic-based privacy model has been proposed until now for privacy preservation of multiple sensitive attributes. In this paper, we propose a novel privacy preserving model F-Classify that uses fuzzy logic for the classification of quasi-identifier and multiple sensitive attributes. Classes are defined based on defined rules, and every tuple is assigned to its class according to attribute value. The working of the F-Classify Algorithm is also verified using HLPN. A wide range of experiments on healthcare data sets acknowledged that F-Classify surpasses its counterparts in terms of privacy and utility. Being based on artificial intelligence, it has a lower execution time than other approaches.

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“…To overcome this drawback, Song et al proposed a privacy preservation scheme ensuring that node labels are indistinguishable sufficiently. Furthermore, Zhu et al proposed a new approach to publish anonymous table against the attacker owning publishing algorithm and some individuals' sensitive attribute values. However, some malicious data users can access structure information to re‐identify individuals or speculate about the relationships between two nodes.…”

Section: Related Workmentioning

confidence: 99%

“…Besides, Salary and Personal health are personal sensitive attributes. According to node information, various preservation schemes have been proposed, which mainly hide these attributes. However, the malicious data users can still obtain data providers' privacy from the structure information.…”

Section: Introductionmentioning

confidence: 99%

A γ‐Strawman privacy‐preserving scheme in weighted social networks

Chen

Zhang

Chen

et al. 2016

Security Comm Networks

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With the dramatic development of social network applications, such as the Facebook, Twitter, and MySpace, privacy‐preserving problem is getting increasingly concerned. Apart from node information, researchers have found that structure information can also leak data providers' privacy, especially in weighted social networks. However, most of the existing private‐preserving schemes focus on a single aspect. The comprehensive consideration introduces two challenges. On one hand, the different anonymity demands of node and structure information lead to the collision of different design criteria, which is called as consistency matching problem. On the other hand, the simple combination of existing schemes may introduce large amounts of unnecessary changes, which makes the published information meaningless. Thus, we must find the balance between anonymity demands and changes, which is called as optimization trade‐off problem. In this paper, we propose a γ‐Strawman privacy‐preserving scheme in weighted social networks to solve these challenges. To address consistency matching problem, we propose a greedy algorithm based on a user trade‐off metric. For optimization tradeoff problem, a closeness edge‐editing technology is considered, which can change the private information slightly. Finally, we evaluate our scheme on real‐world datasets, the experimental results show that the γ‐Strawman scheme is efficient. Copyright © 2017 John Wiley & Sons, Ltd.

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Preserving privacy for sensitive values of individuals in data publishing based on a new additive noise approach

Cited by 9 publications

References 18 publications

Heap Bucketization Anonymity—An Efficient Privacy-Preserving Data Publishing Model for Multiple Sensitive Attributes

Heap Bucketization Anonymity—An Efficient Privacy-Preserving Data Publishing Model for Multiple Sensitive Attributes

F-Classify: Fuzzy Rule Based Classification Method for Privacy Preservation of Multiple Sensitive Attributes

A γ‐Strawman privacy‐preserving scheme in weighted social networks

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