HRR: a data cleaning approach preserving local differential privacy
Qilong Han,
Qianqian Chen,
Liguo Zhang and
Kejia Zhang
International Journal of Distributed Sensor Networks, 2018, vol. 14, issue 12, 1550147718819938
Abstract:
For the sensitive data generated by the sensor, we can use the noise to protect the privacy of these data. However, because of the complicated collection environment of the sensor data, it is easy to obtain some disorderly data, and the data need to be cleaned before use. In this work, we establish the differential privacy cleaning model H-RR, which is based on the contradiction generated by the function dependency, correct the contradictory data, and use the indistinguishability between the correction results to protect the data privacy. In this model, we add the local differential privacy mechanism in the process of data cleaning. While simplifying the data pre-processing process, we want to find a balance between data availability and security.
Keywords: Sensor network; local differential privacy; data cleaning (search for similar items in EconPapers)
Date: 2018
References: View complete reference list from CitEc
Citations:
Downloads: (external link)
https://journals.sagepub.com/doi/10.1177/1550147718819938 (text/html)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:14:y:2018:i:12:p:1550147718819938
DOI: 10.1177/1550147718819938
Access Statistics for this article
More articles in International Journal of Distributed Sensor Networks
Bibliographic data for series maintained by SAGE Publications ().