Duplication with Trapdoor Sensitive Attribute Values: A New Approach for Privacy Preserving Data Publishing

Privacy preserving data publishing has received considerable attention for publishing useful information while preserving data privacy. The existing privacy preserving data publishing methods for multiple sensitive attributes do not consider the situation that different values of a sensitive attribute may have different sensitivity requirements. To solve this problem, we defined three security levels for different sensitive attribute values that have different sensitivity requirements, and given an L s l -diversity model for multiple sensitive attributes. Following this, we proposed three specific greed algorithms based on the maximal-bucket first (MBF), maximal single-dimension-capacity first (MSDCF) and maximal multi-dimension-capacity first (MMDCF) algorithms and the maximal security-level first (MSLF) greed policy, named as MBF based on MSLF (MBF-MSLF), MSDCF based on MSLF (MSDCF-MSLF) and MMDCF based on MSLF (MMDCF-MSLF), to implement the L s l -diversity model for multiple sensitive attributes. The experimental results show that the three algorithms can greatly reduce the information loss of the published microdata, but their runtime is only a small increase, and their information loss tends to be stable with the increasing of data volume. And they can solve the problem that the information loss of MBF, MSDCF and MMDCF increases greatly with the increasing of sensitive attribute number.

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Slicing: A New Approach for Privacy Preserving Data Publishing

IEEE Transactions on Knowledge and Data Engineering ◽

10.1109/tkde.2010.236 ◽

2012 ◽

Vol 24 (3) ◽

pp. 561-574 ◽

Cited By ~ 115

Author(s):

Tiancheng Li ◽

Ninghui Li ◽

Jian Zhang ◽

Ian Molloy

Keyword(s):

Privacy Preserving ◽

Data Publishing ◽

New Approach ◽

Privacy Preserving Data Publishing

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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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Stack and Deal: An Efficient Algorithm for Privacy Preserving Data Publishing

10.5121/csit.2021.111111 ◽

2021 ◽

Author(s):

Vikas Thammanna Gowda

Keyword(s):

Efficient Algorithm ◽

Privacy Preserving ◽

Information Loss ◽

Equivalence Classes ◽

Data Publishing ◽

Sensitive Attribute ◽

Privacy Preserving Data Publishing ◽

Privacy Models

Although k-Anonymity is a good way to publish microdata for research purposes, it still suffers from various attacks. Hence, many refinements of k-Anonymity have been proposed such as ldiversity and t-Closeness, with t-Closeness being one of the strictest privacy models. Satisfying t-Closeness for a lower value of t may yield equivalence classes with high number of records which results in a greater information loss. For a higher value of t, equivalence classes are still prone to homogeneity, skewness, and similarity attacks. This is because equivalence classes can be formed with fewer distinct sensitive attribute values and still satisfy the constraint t. In this paper, we introduce a new algorithm that overcomes the limitations of k-Anonymity and lDiversity and yields equivalence classes of size k with greater diversity and frequency of a SA value in all the equivalence classes differ by at-most one.

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An optimal dynamic KCi-slice model for privacy preserving data publishing of multiple sensitive attributes adopting various sensitivity thresholds

International Journal of Data Science ◽

10.1504/ijds.2019.105264 ◽

2019 ◽

Vol 4 (4) ◽

pp. 320

Author(s):

N.V.S. Lakshmipathi Raju ◽

M.N. Seetaramanath ◽

P. Srinivasa Rao

Keyword(s):

Privacy Preserving ◽

Data Publishing ◽

Optimal Dynamic ◽

Privacy Preserving Data Publishing

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Privacy preserving data publishing of categorical data through k ‐anonymity and feature selection

Healthcare Technology Letters ◽

10.1049/htl.2015.0050 ◽

2016 ◽

Vol 3 (1) ◽

pp. 16-21 ◽

Cited By ~ 10

Author(s):

Aristos Aristodimou ◽

Athos Antoniades ◽

Constantinos S. Pattichis

Keyword(s):

Feature Selection ◽

Categorical Data ◽

Privacy Preserving ◽

Data Publishing ◽

Privacy Preserving Data Publishing

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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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