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INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 9, ISSUE 01, JANUARY 2020ISSN 22778616KAnonymization Approach FOR Privacy Preserving IN Data Mining Vimalkumar B. Safely Abstract:
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How to fill out k-anonymization approach for privacy
How to fill out k-anonymization approach for privacy
01
Identify the sensitive attributes that need to be protected.
02
Group the data based on these sensitive attributes.
03
For each group, ensure that there are at least k-1 other similar records.
04
Generalize or suppress the data to make each group indistinguishable.
05
Repeat the process until all sensitive attributes are adequately protected.
Who needs k-anonymization approach for privacy?
01
Organizations dealing with personal or sensitive information such as healthcare providers, financial institutions, and government agencies.
02
Individuals who want to protect their personal information from data breaches or unauthorized access.
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What is k-anonymization approach for privacy?
K-anonymization is a technique used to protect privacy by making sure that individual data cannot be distinguished from at least k-1 other records in a dataset.
Who is required to file k-anonymization approach for privacy?
Organizations that collect and store personal data are required to use k-anonymization approach for privacy.
How to fill out k-anonymization approach for privacy?
To fill out k-anonymization approach for privacy, organizations need to apply techniques such as generalization and suppression to ensure that individual identities are protected.
What is the purpose of k-anonymization approach for privacy?
The purpose of k-anonymization approach for privacy is to prevent re-identification of individuals in a dataset, thus protecting their privacy.
What information must be reported on k-anonymization approach for privacy?
The information reported on k-anonymization approach for privacy includes the methodology used for anonymization, parameters set for k-anonymity, and any potential risks of re-identification.
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