Effective privacy preserving data publishing by vectorization
| Autor: | Wookey Lee, Chris Soo-Hyun Eom, Charles Cheolgi Lee, Carson K. Leung |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Thesaurus (information retrieval)
Information Systems and Management Computer science business.industry 05 social sciences 050301 education Cloud computing 02 engineering and technology Data publishing Computer security computer.software_genre Grid Computer Science Applications Theoretical Computer Science Dilemma Artificial Intelligence Control and Systems Engineering 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Image tracing business 0503 education computer Software Anonymity |
| Zdroj: | Information Sciences. 527:311-328 |
| ISSN: | 0020-0255 |
| DOI: | 10.1016/j.ins.2019.09.035 |
| Popis: | As smart devices and cloud services are rapidly expanding, a large amount of location information can easily be gathered. However, there is a conflict between collecting location data and protecting personal data since obtaining and utilizing the data may be restricted due to privacy concerns. Various methods for anonymity and on the original location data have been studied, but these methods have excessively reduced data utility while stressing highly on privacy preservation. In this article, we suggest a novel model to overcome this fundamental dilemma via a surrogate vector based on the grid environment. Compared to the existing approaches, our model shows a new theoretical advancement in privacy protection, and outstanding performance with respect to time complexity and data utility has been achieved. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect