HRR: a data cleaning approach preserving local differential privacy
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...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-12-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147718819938 |
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| _version_ | 1832547785794125824 |
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| author | Qilong Han Qianqian Chen Liguo Zhang Kejia Zhang |
| author_facet | Qilong Han Qianqian Chen Liguo Zhang Kejia Zhang |
| author_sort | Qilong Han |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-7945700a075f41628ab6b0f71ec3f1c9 |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2018-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-7945700a075f41628ab6b0f71ec3f1c92025-02-03T06:43:17ZengWileyInternational Journal of Distributed Sensor Networks1550-14772018-12-011410.1177/1550147718819938HRR: a data cleaning approach preserving local differential privacyQilong HanQianqian ChenLiguo ZhangKejia ZhangFor 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.https://doi.org/10.1177/1550147718819938 |
| spellingShingle | Qilong Han Qianqian Chen Liguo Zhang Kejia Zhang HRR: a data cleaning approach preserving local differential privacy International Journal of Distributed Sensor Networks |
| title | HRR: a data cleaning approach preserving local differential privacy |
| title_full | HRR: a data cleaning approach preserving local differential privacy |
| title_fullStr | HRR: a data cleaning approach preserving local differential privacy |
| title_full_unstemmed | HRR: a data cleaning approach preserving local differential privacy |
| title_short | HRR: a data cleaning approach preserving local differential privacy |
| title_sort | hrr a data cleaning approach preserving local differential privacy |
| url | https://doi.org/10.1177/1550147718819938 |
| work_keys_str_mv | AT qilonghan hrradatacleaningapproachpreservinglocaldifferentialprivacy AT qianqianchen hrradatacleaningapproachpreservinglocaldifferentialprivacy AT liguozhang hrradatacleaningapproachpreservinglocaldifferentialprivacy AT kejiazhang hrradatacleaningapproachpreservinglocaldifferentialprivacy |