A Symmetry Histogram Publishing Method Based on Differential Privacy
The differential privacy histogram publishing method based on grouping cannot balance the grouping reconstruction error and Laplace noise error, resulting in insufficient histogram publishing accuracy. To address this problem, we propose a symmetric histogram publishing method DPHR (differential pri...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-05-01
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| Series: | Symmetry |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-8994/15/5/1099 |
| _version_ | 1797598268704686080 |
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| author | Tao Tao Siwen Li Jun Huang Shudong Hou Huajun Gong |
| author_facet | Tao Tao Siwen Li Jun Huang Shudong Hou Huajun Gong |
| author_sort | Tao Tao |
| collection | DOAJ |
| description | The differential privacy histogram publishing method based on grouping cannot balance the grouping reconstruction error and Laplace noise error, resulting in insufficient histogram publishing accuracy. To address this problem, we propose a symmetric histogram publishing method DPHR (differential privacy histogram released). Firstly, the algorithm uses the exponential mechanism to sort the counting of the original histogram bucket globally to improve the grouping accuracy; secondly, we propose an optimal dynamic symmetric programming grouping algorithm based on the global minimum error, which uses the global minimum error as the error evaluation function based on the ordered histogram. This way, we can achieve a global grouping of the optimal error balance while balancing the reconstruction and Laplace errors. Experiments show that this method effectively reduces the cumulative error between the published histogram and the original histogram under long-range counting queries based on satisfying <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>ε</mi></semantics></math></inline-formula>-differential privacy and improves the usability of the published histogram data. |
| first_indexed | 2024-03-11T03:17:01Z |
| format | Article |
| id | doaj.art-f371d87b79564ab9ad0cc5c5389a6527 |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-03-11T03:17:01Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-f371d87b79564ab9ad0cc5c5389a65272023-11-18T03:30:55ZengMDPI AGSymmetry2073-89942023-05-01155109910.3390/sym15051099A Symmetry Histogram Publishing Method Based on Differential PrivacyTao Tao0Siwen Li1Jun Huang2Shudong Hou3Huajun Gong4College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaSchool of Computer Science and Technology, Anhui University of Technology, Ma’anshan 243032, ChinaSchool of Computer Science and Technology, Anhui University of Technology, Ma’anshan 243032, ChinaSchool of Computer Science and Technology, Anhui University of Technology, Ma’anshan 243032, ChinaCollege of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaThe differential privacy histogram publishing method based on grouping cannot balance the grouping reconstruction error and Laplace noise error, resulting in insufficient histogram publishing accuracy. To address this problem, we propose a symmetric histogram publishing method DPHR (differential privacy histogram released). Firstly, the algorithm uses the exponential mechanism to sort the counting of the original histogram bucket globally to improve the grouping accuracy; secondly, we propose an optimal dynamic symmetric programming grouping algorithm based on the global minimum error, which uses the global minimum error as the error evaluation function based on the ordered histogram. This way, we can achieve a global grouping of the optimal error balance while balancing the reconstruction and Laplace errors. Experiments show that this method effectively reduces the cumulative error between the published histogram and the original histogram under long-range counting queries based on satisfying <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>ε</mi></semantics></math></inline-formula>-differential privacy and improves the usability of the published histogram data.https://www.mdpi.com/2073-8994/15/5/1099differential privacyhistogramglobal errordynamic programming |
| spellingShingle | Tao Tao Siwen Li Jun Huang Shudong Hou Huajun Gong A Symmetry Histogram Publishing Method Based on Differential Privacy Symmetry differential privacy histogram global error dynamic programming |
| title | A Symmetry Histogram Publishing Method Based on Differential Privacy |
| title_full | A Symmetry Histogram Publishing Method Based on Differential Privacy |
| title_fullStr | A Symmetry Histogram Publishing Method Based on Differential Privacy |
| title_full_unstemmed | A Symmetry Histogram Publishing Method Based on Differential Privacy |
| title_short | A Symmetry Histogram Publishing Method Based on Differential Privacy |
| title_sort | symmetry histogram publishing method based on differential privacy |
| topic | differential privacy histogram global error dynamic programming |
| url | https://www.mdpi.com/2073-8994/15/5/1099 |
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