An Improved Data Anonymization Algorithm for Incomplete Medical Dataset Publishing

2019 ◽  
pp. 115-128
Author(s):  
Wei Liu ◽  
Mengli Pei ◽  
Congcong Cheng ◽  
Wei She ◽  
Chase Q. Wu
Keyword(s):  
Data Anonymization ◽  
Medical Dataset

Big Data Privacy Preservation Using Two Phase Top-Down Specialization Algorithm with Multidimensional Map Reduce Framework on Hadoop

2015 ◽  
Vol 3 (2) ◽  
Author(s):  
Shalin Eliabeth S. ◽  
Sarju S.
Keyword(s):  
Big Data ◽  
Data Privacy ◽  
Privacy Preservation ◽  
Experimental Result ◽  
Map Reduce ◽  
Distributed Environment ◽  
Top Down ◽  
Two Phase ◽  
Data Anonymization ◽  
Big Data Privacy

Big data privacy preservation is one of the most disturbed issues in current industry. Sometimes the data privacy problems never identified when input data is published on cloud environment. Data privacy preservation in hadoop deals in hiding and publishing input dataset to the distributed environment. In this paper investigate the problem of big data anonymization for privacy preservation from the perspectives of scalability and time factor etc. At present, many cloud applications with big data anonymization faces the same kind of problems. For recovering this kind of problems, here introduced a data anonymization algorithm called Two Phase Top-Down Specialization (TPTDS) algorithm that is implemented in hadoop. For the data anonymization-45,222 records of adults information with 15 attribute values was taken as the input big data. With the help of multidimensional anonymization in map reduce framework, here implemented proposed Two-Phase Top-Down Specialization anonymization algorithm in hadoop and it will increases the efficiency on the big data processing system. By conducting experiment in both one dimensional and multidimensional map reduce framework with Two Phase Top-Down Specialization algorithm on hadoop, the better result shown in multidimensional anonymization on input adult dataset. Data sets is generalized in a top-down manner and the better result was shown in multidimensional map reduce framework by the better IGPL values generated by the algorithm. The anonymization was performed with specialization operation on taxonomy tree. The experiment shows that the solutions improves the IGPL values, anonymity parameter and decreases the execution time of big data privacy preservation by compared to the existing algorithm. This experimental result will leads to great application to the distributed environment.

Classification of Medical Dataset Along with Topic Modeling Using LDA

2018 ◽  
pp. 1-11 ◽  
Author(s):  
M. Selvi ◽  
K. Thangaramya ◽  
M. S. Saranya ◽  
K. Kulothungan ◽  
S. Ganapathy ◽  
...  
Keyword(s):  
Topic Modeling ◽  
Medical Dataset

scholarly journals A 3-D Mixed-Reality System for Stereoscopic Visualization of Medical Dataset

2009 ◽  
Vol 56 (11) ◽  
pp. 2627-2633 ◽  
Author(s):  
V. Ferrari ◽  
G. Megali ◽  
E. Troia ◽  
A. Pietrabissa ◽  
F. Mosca
Keyword(s):  
Mixed Reality ◽  
Stereoscopic Visualization ◽  
Medical Dataset

scholarly journals Population-Based Linkage of Big Data in Dental Research

2018 ◽  
Vol 15 (11) ◽  
pp. 2357 ◽  
Author(s):  
Tim Joda ◽  
Tuomas Waltimo ◽  
Christiane Pauli-Magnus ◽  
Nicole Probst-Hensch ◽  
Nicola Zitzmann
Keyword(s):  
Clinical Trials ◽  
Big Data ◽  
Healthcare Services ◽  
Population Based ◽  
Services Research ◽  
Research Education ◽  
Data Anonymization ◽  
Dental Research ◽  
Epidemiological Surveys ◽  
Patient Consent

Population-based linkage of patient-level information opens new strategies for dental research to identify unknown correlations of diseases, prognostic factors, novel treatment concepts and evaluate healthcare systems. As clinical trials have become more complex and inefficient, register-based controlled (clinical) trials (RC(C)T) are a promising approach in dental research. RC(C)Ts provide comprehensive information on hard-to-reach populations, allow observations with minimal loss to follow-up, but require large sample sizes with generating high level of external validity. Collecting data is only valuable if this is done systematically according to harmonized and inter-linkable standards involving a universally accepted general patient consent. Secure data anonymization is crucial, but potential re-identification of individuals poses several challenges. Population-based linkage of big data is a game changer for epidemiological surveys in Public Health and will play a predominant role in future dental research by influencing healthcare services, research, education, biotechnology, insurance, social policy and governmental affairs.

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