A New View of Privacy in Social Networks

2016 ◽

pp. 28-51 ◽

Cited By ~ 1

Author(s):

Wei Chang ◽

Jie Wu

Keyword(s):

Privacy Protection ◽

Privacy Preserving ◽

Propagation Path ◽

Data Publishing ◽

Mobile Social Network ◽

Data Utility ◽

Privacy Requirements ◽

Individual Privacy ◽

Utility Loss ◽

Privacy Preserving Data Publishing

Many smartphone-based applications need microdata, but publishing a microdata table may leak respondents' privacy. Conventional researches on privacy-preserving data publishing focus on providing identical privacy protection to all data requesters. Considering that, instead of trapping in a small coterie, information usually propagates from friend to friend. The authors study the privacy-preserving data publishing problem on a mobile social network. Along a propagation path, a series of tables will be locally created at each participant, and the tables' privacy-levels should be gradually enhanced. However, the tradeoff between these tables' overall utility and their individual privacy requirements are not trivial: any inappropriate sanitization operation under a lower privacy requirement may cause dramatic utility loss on the subsequent tables. For solving the problem, the authors propose an approximation algorithm by previewing the future privacy requirements. Extensive results show that this approach successfully increases the overall data utility, and meet the strengthening privacy requirements.

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Anonymization Based on Improved Bucketization (AIB): A Privacy-Preserving Data Publishing Technique for Improving Data Utility in Healthcare Data

Journal of Medical Imaging and Health Informatics ◽

10.1166/jmihi.2021.3901 ◽

2021 ◽

Vol 11 (12) ◽

pp. 3164-3173

Author(s):

R. Indhumathi ◽

S. Sathiya Devi

Keyword(s):

Medical Information ◽

Threshold Value ◽

Privacy Preserving ◽

Data Publishing ◽

Published Data ◽

Sensitive Information ◽

Data Utility ◽

Healthcare Data ◽

Privacy Preserving Data Publishing ◽

Horizontal Partitioning

Data sharing is essential in present biomedical research. A large quantity of medical information is gathered and for different objectives of analysis and study. Because of its large collection, anonymity is essential. Thus, it is quite important to preserve privacy and prevent leakage of sensitive information of patients. Most of the Anonymization methods such as generalisation, suppression and perturbation are proposed to overcome the information leak which degrades the utility of the collected data. During data sanitization, the utility is automatically diminished. Privacy Preserving Data Publishing faces the main drawback of maintaining tradeoff between privacy and data utility. To address this issue, an efficient algorithm called Anonymization based on Improved Bucketization (AIB) is proposed, which increases the utility of published data while maintaining privacy. The Bucketization technique is used in this paper with the intervention of the clustering method. The proposed work is divided into three stages: (i) Vertical and Horizontal partitioning (ii) Assigning Sensitive index to attributes in the cluster (iii) Verifying each cluster against privacy threshold (iv) Examining for privacy breach in Quasi Identifier (QI). To increase the utility of published data, the threshold value is determined based on the distribution of elements in each attribute, and the anonymization method is applied only to the specific QI element. As a result, the data utility has been improved. Finally, the evaluation results validated the design of paper and demonstrated that our design is effective in improving data utility.

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f-Slip: An Efficient Privacy-Preserving Data Publishing Framework for 1: M Microdata with Multiple Sensitive Attributes.

10.21203/rs.3.rs-660451/v1 ◽

2021 ◽

Author(s):

Jayapradha J ◽

Prakash M

Keyword(s):

Privacy Preserving ◽

Vital Role ◽

Data Publishing ◽

Slip Model ◽

Correlation Attack ◽

Sensitive Attribute ◽

Utility Loss ◽

Privacy Preserving Data Publishing ◽

Loss Efficiency ◽

Attribute Correlation

Abstract Privacy of the individuals plays a vital role when a dataset is disclosed in public. Privacy-preserving data publishing is a process of releasing the anonymized dataset for various purposes of analysis and research. The data to be published contain several sensitive attributes such as diseases, salary, symptoms, etc. Earlier, researchers have dealt with datasets considering it would contain only one record for an individual [1:1 dataset], which is uncompromising in various applications. Later, many researchers concentrate on the dataset, where an individual has multiple records [1:M dataset]. In the paper, a model f-slip was proposed that can address the various attacks such as Background Knowledge (bk) attack, Multiple Sensitive attribute correlation attack (MSAcorr), Quasi-identifier correlation attack(QIcorr), Non-membership correlation attack(NMcorr) and Membership correlation attack(Mcorr) in 1:M dataset and the solutions for the attacks. In f -slip, the anatomization was performed to divide the table into two subtables consisting of i) quasi-identifier and ii) sensitive attributes. The correlation of sensitive attributes is computed to anonymize the sensitive attributes without breaking the linking relationship. Further, the quasi-identifier table was divided and k-anonymity was implemented on it. An efficient anonymization technique, frequency-slicing (f-slicing), was also developed to anonymize the sensitive attributes. The f -slip model is consistent as the number of records increases. Extensive experiments were performed on a real-world dataset Informs and proved that the f -slip model outstrips the state-of-the-art techniques in terms of utility loss, efficiency and also acquires an optimal balance between privacy and utility.

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Anonymization on refining partition: Same privacy, more utility

Computer Science and Information Systems ◽

10.2298/csis141212052z ◽

2015 ◽

Vol 12 (4) ◽

pp. 1193-1216 ◽

Cited By ~ 1

Author(s):

Hong Zhu ◽

Shengli Tian ◽

Genyuan Du ◽

Meiyi Xie

Keyword(s):

Experimental Evaluation ◽

Privacy Preserving ◽

Data Publishing ◽

Data Utility ◽

Sensitive Attribute ◽

The Optimizing Model ◽

Privacy Preserving Data Publishing ◽

Initial Partition ◽

Optimizing Model

In privacy preserving data publishing, to reduce the correlation loss between sensitive attribute (SA) and non-sensitive attributes(NSAs) caused by anonymization methods (such as generalization, anatomy, slicing and randomization, etc.), the records with same NSAs values should be divided into same blocks to meet the anonymizing demands of ?-diversity. However, there are often many blocks (of the initial partition), in which there are more than ? records with different SA values, and the frequencies of different SA values are uneven. Therefore, anonymization on the initial partition causes more correlation loss. To reduce the correlation loss as far as possible, in this paper, an optimizing model is first proposed. Then according to the optimizing model, the refining partition of the initial partition is generated, and anonymization is applied on the refining partition. Although anonymization on refining partition can be used on top of any existing partitioning method to reduce the correlation loss, we demonstrate that a new partitioning method tailored for refining partition could further improve data utility. An experimental evaluation shows that our approach could efficiently reduce correlation loss.

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