scholarly journals General Confidentiality and Utility Metrics for Privacy-Preserving Data Publishing Based on the Permutation Model

2020 ◽  
pp. 1-1 ◽  
Author(s):  
Josep Domingo-Ferrer ◽  
Krishnamurty Muralidhar ◽  
Maria Bras-Amoros
Keyword(s):  
Privacy Preserving ◽  
Data Publishing ◽  
Permutation Model ◽  
Privacy Preserving Data Publishing ◽  
Utility Metrics

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 Privacy preserving data publishing: Review

2015 ◽  
Vol 10 (7) ◽  
pp. 239-247 ◽  
Author(s):  
Hatem Rashid Asmaa ◽  
Binti Mohd Yasin Norizan
Keyword(s):  
Privacy Preserving ◽  
Data Publishing ◽  
Privacy Preserving Data Publishing

Amelioration of Anonymity Modus Operandi for Privacy Preserving Data Publishing

2014 ◽  
pp. 96-107 ◽  
Author(s):  
J. Indumathi
Keyword(s):  
Performance Measures ◽  
Privacy Preserving ◽  
Research Community ◽  
Data Publishing ◽  
Modus Operandi ◽  
Privacy Preserving Data Mining ◽  
Research Problems ◽  
Privacy Preserving Data Publishing ◽  
Cryptographic Techniques ◽  
Privacy Issues

The scientific tumultuous intonation has swept our feet's, of its balance and at the same time wheedled us to reach the take-off arena from where we can march equipped and outfitted into the subsequent century with confidence & self-assurance; by unearthing solutions for all information security related issues (with special emphasis on privacy issues). Examining various outstanding research problems that encompass to be embarked upon for effectively managing and controlling the balance between privacy and utility, the research community is pressurized to propose suitable elucidations. The solution is to engender several Privacy-Preserving Data Publishing (PPDP) techniques like Perturbation, swapping, randomization, cryptographic techniques etc., Amongst the various available techniques k-anonymity is unique in facet of its association with protection techniques that preserve the truthfulness of the data. The principal chip in of this sketch out comprises: 1) Motivation for this exploration for Amelioration Of Anonymity Modus Operandi For Privacy Preserving Data Mining; 2) investigation of well-known research approaches to PPDM; 3) argue solutions to tackle the problems of security threats and attacks in the PPDM in systems; 4) related survey of the various anonymity techniques; 5) exploration of metrics for the diverse anonymity techniques; 6) performance measures for the various anonymity techniques; and 7) contradistinguish the diverse anonymity techniques and algorithms.

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.

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