scholarly journals A General Framework Information Loss of Utility-Based Anonymization in Data Publishing

2021 ◽  
Vol 12 (5) ◽  
pp. 1450-1456
Author(s):  
Waleed M.Ead, Et. al.
Keyword(s):  
General Framework ◽  
Background Knowledge ◽  
Information Loss ◽  
Data Publishing ◽  
Trade Off ◽  
Information Utility ◽  
Information Usage ◽  
Anonymized Data ◽  
Privacy Level

To build anonymization, the data anonymizer must determine the following three issues: Firstly, which data to be preserved? Secondly, which adversary background knowledge used to disclosure the anonymized data? Thirdly, The usage of the anonymized data? We have different anonymization techniques from the previous three-question according to different adversary background knowledge and information usage (information utility). In other words, different anonymization techniques lead to different information loss. In this paper, we propose a general framework for the utility-based anonymization to minimize the information loss in data published with a trade-off grantee of achieving the required privacy level.

Analytical Study on Privacy Attack Models in Privacy Preserving Data Publishing

2019 ◽  
pp. 1518-1538
Author(s):  
Sowmyarani C. N. ◽  
Dayananda P.
Keyword(s):  
Private Information ◽  
Background Knowledge ◽  
Privacy Preserving ◽  
Data Publishing ◽  
Background Information ◽  
Published Data ◽  
Privacy Attack ◽  
Privacy Preserving Data Publishing ◽  
Available Information ◽  
Attack Models

Privacy attack on individual records has great concern in privacy preserving data publishing. When an intruder who is interested to know the private information of particular person of his interest, will acquire background knowledge about the person. This background knowledge may be gained though publicly available information such as Voter's id or through social networks. Combining this background information with published data; intruder may get the private information causing a privacy attack of that person. There are many privacy attack models. Most popular attack models are discussed in this chapter. The study of these attack models plays a significant role towards the invention of robust Privacy preserving models.

scholarly journals The interspecific growth–mortality trade-off is not a general framework for tropical forest community structure

2020 ◽  
Author(s):  
Sabrina E. Russo ◽  
Sean M. McMahon ◽  
Matteo Detto ◽  
Glenn Ledder ◽  
S. Joseph Wright ◽  
...  
Keyword(s):  
Community Structure ◽  
Tropical Forest ◽  
General Framework ◽  
Forest Community ◽  
Trade Off

scholarly journals Privacy Preserving Data Publishing for Multiple Sensitive Attributes Based on Security Level

Information ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 166
Author(s):  
Yuelei Xiao ◽  
Haiqi Li
Keyword(s):  
Data Privacy ◽  
Privacy Preserving ◽  
Information Loss ◽  
Experimental Results ◽  
Data Publishing ◽  
Security Level ◽  
Sensitive Attribute ◽  
Data Volume ◽  
Security Levels ◽  
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.

Scalable l-Diversity

2019 ◽  
Vol 14 (2) ◽  
pp. 27-40
Author(s):  
Udai Pratap Rao ◽  
Brijesh B. Mehta ◽  
Nikhil Kumar
Keyword(s):  
Equivalence Class ◽  
Information Loss ◽  
Equivalence Classes ◽  
Data Publishing ◽  
Programming Paradigm ◽  
Research Areas ◽  
Complete Dataset ◽  
Input Dataset ◽  
Privacy Preserving Data Publishing ◽  
Cost Penalty

Privacy preserving data publishing is one of the most demanding research areas in the recent few years. There are more than billions of devices capable to collect the data from various sources. To preserve the privacy while publishing data, algorithms for equivalence class generation and scalable anonymization with k-anonymity and l-diversity using MapReduce programming paradigm are proposed in this article. Equivalence class generation algorithms divide the datasets into equivalence classes for Scalable k-Anonymity (SKA) and Scalable l-Diversity (SLD) separately. These equivalence classes are finally fed to the anonymization algorithm that calculates the Gross Cost Penalty (GCP) for the complete dataset. The value of GCP gives information loss in input dataset after anonymization.

scholarly journals Novel Method to Watermark Anonymized Data for Data Publishing

2017 ◽  
Vol E100.D (8) ◽  
pp. 1671-1679 ◽  
Author(s):  
Yuichi NAKAMURA ◽  
Yoshimichi NAKATSUKA ◽  
Hiroaki NISHI
Keyword(s):  
Data Publishing ◽  
Anonymized Data ◽  
Novel Method

scholarly journals Privacy Preserving Data Publishing with Multiple Sensitive Attributes based on Overlapped Slicing

Information ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 362
Author(s):  
Widodo ◽  
Eko Kuswardono Budiardjo ◽  
Wahyu Catur Wibowo
Keyword(s):  
Privacy Preserving ◽  
Information Loss ◽  
Data Publishing ◽  
Slicing Method ◽  
Privacy Preserving Data Publishing

Investigation into privacy preserving data publishing with multiple sensitive attributes is performed to reduce probability of adversaries to guess the sensitive values. Masking the sensitive values is usually performed by anonymizing data by using generalization and suppression techniques. A successful anonymization technique should reduce information loss due to the generalization and suppression. This research attempts to solve both problems in microdata with multiple sensitive attributes. We propose a novel overlapped slicing method for privacy preserving data publishing with multiple sensitive attributes. We used discernibility metrics to measure information loss. The experiment result shows that our method obtained a lower discernibility value than other methods.

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