Application of differential privacy to sensor data in water quality monitoring task
Although differential privacy (DP) is used to obfuscate local information and avoid data leakage, very little research exists on the neural network model performance with applied DP for datasets from online drinking water sensor monitoring. This study aims to examine the accuracy of four different n...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2025-05-01
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| Series: | Ecological Informatics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1574954125000287 |
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| _version_ | 1832576482106408960 |
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| author | Audris Arzovs Sergei Parshutin Valts Urbanovics Janis Rubulis Sandis Dejus |
| author_facet | Audris Arzovs Sergei Parshutin Valts Urbanovics Janis Rubulis Sandis Dejus |
| author_sort | Audris Arzovs |
| collection | DOAJ |
| description | Although differential privacy (DP) is used to obfuscate local information and avoid data leakage, very little research exists on the neural network model performance with applied DP for datasets from online drinking water sensor monitoring. This study aims to examine the accuracy of four different neural network model architectures with DP applications. To compare the performance of the neural network model performance in total 2 215 906 augmented and experimentally obtained sensor readings were obtained from a drinking-water pilot system. Three types of contaminations at three different concentrations were applied as scenarios for anomalies in drinking water monitoring. The results achieved similar accuracy with all model architectures, with the best result showing only a 0.3% reduction in model accuracy compared with a nonprivate neural network model with 94% and 94.7% accuracy, respectively. Thus, differential privacy can be applied in the field of water quality monitoring with a reasonable decrease in the model performance. |
| format | Article |
| id | doaj-art-71d635048bc546abaa288936075df593 |
| institution | Kabale University |
| issn | 1574-9541 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Informatics |
| spelling | doaj-art-71d635048bc546abaa288936075df5932025-01-31T05:10:57ZengElsevierEcological Informatics1574-95412025-05-0186103019Application of differential privacy to sensor data in water quality monitoring taskAudris Arzovs0Sergei Parshutin1Valts Urbanovics2Janis Rubulis3Sandis Dejus4Institute of Electronics and Computer Science, Riga, Latvia; Corresponding authors.Institute of Information Technology, Riga Technical University, Riga, LatviaWater Systems and Biotechnology institute, Riga Technical University, Riga, LatviaWater Systems and Biotechnology institute, Riga Technical University, Riga, LatviaWater Systems and Biotechnology institute, Riga Technical University, Riga, Latvia; Corresponding authors.Although differential privacy (DP) is used to obfuscate local information and avoid data leakage, very little research exists on the neural network model performance with applied DP for datasets from online drinking water sensor monitoring. This study aims to examine the accuracy of four different neural network model architectures with DP applications. To compare the performance of the neural network model performance in total 2 215 906 augmented and experimentally obtained sensor readings were obtained from a drinking-water pilot system. Three types of contaminations at three different concentrations were applied as scenarios for anomalies in drinking water monitoring. The results achieved similar accuracy with all model architectures, with the best result showing only a 0.3% reduction in model accuracy compared with a nonprivate neural network model with 94% and 94.7% accuracy, respectively. Thus, differential privacy can be applied in the field of water quality monitoring with a reasonable decrease in the model performance.http://www.sciencedirect.com/science/article/pii/S1574954125000287Water quality monitoringDifferential privacyFederated learning |
| spellingShingle | Audris Arzovs Sergei Parshutin Valts Urbanovics Janis Rubulis Sandis Dejus Application of differential privacy to sensor data in water quality monitoring task Ecological Informatics Water quality monitoring Differential privacy Federated learning |
| title | Application of differential privacy to sensor data in water quality monitoring task |
| title_full | Application of differential privacy to sensor data in water quality monitoring task |
| title_fullStr | Application of differential privacy to sensor data in water quality monitoring task |
| title_full_unstemmed | Application of differential privacy to sensor data in water quality monitoring task |
| title_short | Application of differential privacy to sensor data in water quality monitoring task |
| title_sort | application of differential privacy to sensor data in water quality monitoring task |
| topic | Water quality monitoring Differential privacy Federated learning |
| url | http://www.sciencedirect.com/science/article/pii/S1574954125000287 |
| work_keys_str_mv | AT audrisarzovs applicationofdifferentialprivacytosensordatainwaterqualitymonitoringtask AT sergeiparshutin applicationofdifferentialprivacytosensordatainwaterqualitymonitoringtask AT valtsurbanovics applicationofdifferentialprivacytosensordatainwaterqualitymonitoringtask AT janisrubulis applicationofdifferentialprivacytosensordatainwaterqualitymonitoringtask AT sandisdejus applicationofdifferentialprivacytosensordatainwaterqualitymonitoringtask |