Open AccessL-Diversity for Data Analysis: Data Swapping with Customized Clustering
Author(s) -
Lingala Thirupathi,
Chandan K. Reddy,
B. V. Ramana Murthy,
Rajashekar Shastry,
Yvss Pragathi
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2089/1/012050
Subject(s) - workflow , computer science , data anonymization , data mining , cluster analysis , information retrieval , data science , information privacy , database , machine learning , computer security
Data anonymization should support the analysts who intend to use the anonymized data. Releasing datasets that contain personal information requires anonymization that balances privacy concerns while preserving the utility of the data. This work shows how choosing anonymization techniques with the data analyst requirements in mind improves effectiveness quantitatively, by minimizing the discrepancy between querying the original data versus the anonymized result, and qualitatively, by simplifying the workflow for querying the data.