An Anonymization Method to Improve Data Utility for Classification

2017 ◽  
pp. 57-71 ◽  
Author(s):  
Jianmin Han ◽  
Juan Yu ◽  
Jianfeng Lu ◽  
Hao Peng ◽  
Jiandang Wu
Keyword(s):  
Data Utility

scholarly journals Achieving High Data Utility K-Anonymization Using Similarity-Based Clustering Model

2016 ◽  
Vol E99.D (8) ◽  
pp. 2069-2078 ◽  
Author(s):  
Mohammad Rasool SARRAFI AGHDAM ◽  
Noboru SONEHARA
Keyword(s):  
Data Utility ◽  
High Data ◽  
Clustering Model

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

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.

scholarly journals Staged Incentive and Punishment Mechanism for Mobile Crowd Sensing

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2391 ◽  
Author(s):  
Dan Tao ◽  
Shan Zhong ◽  
Hong Luo
Keyword(s):  
Incentive Mechanism ◽  
Extensive Study ◽  
Mobile Crowd Sensing ◽  
Space Correlation ◽  
Data Utility ◽  
Sensing Data ◽  
Crowd Sensing ◽  
Time Space ◽  
Punishment Mechanism ◽  
Mobile Crowd

Having an incentive mechanism is crucial for the recruitment of mobile users to participate in a sensing task and to ensure that participants provide high-quality sensing data. In this paper, we investigate a staged incentive and punishment mechanism for mobile crowd sensing. We first divide the incentive process into two stages: the recruiting stage and the sensing stage. In the recruiting stage, we introduce the payment incentive coefficient and design a Stackelberg-based game method. The participants can be recruited via game interaction. In the sensing stage, we propose a sensing data utility algorithm in the interaction. After the sensing task, the winners can be filtered out using data utility, which is affected by time–space correlation. In particular, the participants’ reputation accumulation can be carried out based on data utility, and a punishment mechanism is presented to reduce the waste of payment costs caused by malicious participants. Finally, we conduct an extensive study of our solution based on realistic data. Extensive experiments show that compared to the existing positive auction incentive mechanism (PAIM) and reverse auction incentive mechanism (RAIM), our proposed staged incentive mechanism (SIM) can effectively extend the incentive behavior from the recruiting stage to the sensing stage. It not only achieves being a real-time incentive in both the recruiting and sensing stages but also improves the utility of sensing data.

scholarly journals Examining Disclosure Risk and Data Utility: An Administrative Data Case Study

2014 ◽  
Vol 9 (1) ◽  
pp. 12-24
Author(s):  
Michael Comerford
Keyword(s):  
Administrative Data ◽  
Real World ◽  
Data Sources ◽  
Sufficient Data ◽  
Ethical Challenges ◽  
Data Utility ◽  
Disclosure Risk ◽  
Individual Privacy

The plethora of new data sources, combined with a growing interest in increased access to previously unpublished data, poses a set of ethical challenges regarding individual privacy. This paper sets out one aspect of those challenges: the need to anonymise data in such a form that protects the privacy of individuals while providing sufficient data utility for data users. This issue is discussed using a case study of Scottish Government’s administrative data, in which disclosure risk is examined and data utility is assessed using a potential ‘real-world’ analysis.

scholarly journals Differentially Private Attributed Network Releasing Based on Early Fusion

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Yuye Wang ◽  
Jing Yang ◽  
Jianpei Zhan
Keyword(s):  
Topological Structure ◽  
Differential Privacy ◽  
Probability Model ◽  
Prior Work ◽  
Data Utility ◽  
High Data ◽  
Early Fusion ◽  
Attributed Network ◽  
Analysis Of Social Networks ◽  
Attributed Networks

Vertex attributes exert huge impacts on the analysis of social networks. Since the attributes are often sensitive, it is necessary to seek effective ways to protect the privacy of graphs with correlated attributes. Prior work has focused mainly on the graph topological structure and the attributes, respectively, and combining them together by defining the relevancy between them. However, these methods need to add noise to them, respectively, and they produce a large number of required noise and reduce the data utility. In this paper, we introduce an approach to release graphs with correlated attributes under differential privacy based on early fusion. We combine the graph topological structure and the attributes together with a private probability model and generate a synthetic network satisfying differential privacy. We conduct extensive experiments to demonstrate that our approach could meet the request of attributed networks and achieve high data utility.

A New View of Privacy in Social Networks

2019 ◽  
pp. 517-541
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.

Sign in / Sign up

Export Citation Format

Share Document