An Efficient and Differential Privacy-Based Scheme for Aggregating Mobility Datasets
Mobile smart devices, such as mobile phones, wearable devices, and in-vehicle navigation systems, bring us convenience and have become necessities in modern daily life. The built-in global positioning system (GPS) of these mobile devices collects the users’ mobility data to support path planning, na...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi-Wiley
2024-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2024/5374764 |
| _version_ | 1827077642236461056 |
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| author | Qing Yang Fujun Ji Fei Liu |
| author_facet | Qing Yang Fujun Ji Fei Liu |
| author_sort | Qing Yang |
| collection | DOAJ |
| description | Mobile smart devices, such as mobile phones, wearable devices, and in-vehicle navigation systems, bring us convenience and have become necessities in modern daily life. The built-in global positioning system (GPS) of these mobile devices collects the users’ mobility data to support path planning, navigation and other location-related applications, which also inevitably causes privacy issues. Previous research has shown that employing count-min sketch (CMS) to aggregate mobility datasets is a valid privacy-preserving method for resisting the reconstruction attack on population distributions. However, as the utility/accessibility of the protected datasets is excessively correlated with the size of CMS, decreasing the data transmission cost has become an unsolved issue of that approach. In this paper, we propose an efficient scheme with differential privacy to protect mobility datasets, which releases the privacy-preserving population distributions and achieves better utility as well as a much smaller data transmission cost compared to the CMS-based method. Our proposed scheme is comprised of two collaborative components, global sketch and temporal sketch. The global sketch is responsible for aggregating the raw mobility data and decreasing the data transmission cost, while the temporal sketch is in charge of guaranteeing the utility of the population distributions aggregated by the global sketch. Besides, to enhance the privacy preservation, we employ the Laplace mechanism to make the transmitted data satisfy ϵ-differential privacy. Through our analysis and empirical experiments, compared to the other three state-of-the-art privacy-preserving methods on mobility datasets, our scheme could preserve the privacy of the mobility datasets with much less data transmission cost under the same utility loss. |
| first_indexed | 2024-04-24T23:27:11Z |
| format | Article |
| id | doaj.art-80b6cb408f3e438daa5cedd8dd1daf01 |
| institution | Directory Open Access Journal |
| issn | 2042-3195 |
| language | English |
| last_indexed | 2025-03-20T02:17:01Z |
| publishDate | 2024-01-01 |
| publisher | Hindawi-Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj.art-80b6cb408f3e438daa5cedd8dd1daf012024-10-03T07:51:04ZengHindawi-WileyJournal of Advanced Transportation2042-31952024-01-01202410.1155/2024/5374764An Efficient and Differential Privacy-Based Scheme for Aggregating Mobility DatasetsQing Yang0Fujun Ji1Fei Liu2School of Management and EngineeringSchool of Management and EngineeringSchool of Management and EngineeringMobile smart devices, such as mobile phones, wearable devices, and in-vehicle navigation systems, bring us convenience and have become necessities in modern daily life. The built-in global positioning system (GPS) of these mobile devices collects the users’ mobility data to support path planning, navigation and other location-related applications, which also inevitably causes privacy issues. Previous research has shown that employing count-min sketch (CMS) to aggregate mobility datasets is a valid privacy-preserving method for resisting the reconstruction attack on population distributions. However, as the utility/accessibility of the protected datasets is excessively correlated with the size of CMS, decreasing the data transmission cost has become an unsolved issue of that approach. In this paper, we propose an efficient scheme with differential privacy to protect mobility datasets, which releases the privacy-preserving population distributions and achieves better utility as well as a much smaller data transmission cost compared to the CMS-based method. Our proposed scheme is comprised of two collaborative components, global sketch and temporal sketch. The global sketch is responsible for aggregating the raw mobility data and decreasing the data transmission cost, while the temporal sketch is in charge of guaranteeing the utility of the population distributions aggregated by the global sketch. Besides, to enhance the privacy preservation, we employ the Laplace mechanism to make the transmitted data satisfy ϵ-differential privacy. Through our analysis and empirical experiments, compared to the other three state-of-the-art privacy-preserving methods on mobility datasets, our scheme could preserve the privacy of the mobility datasets with much less data transmission cost under the same utility loss.http://dx.doi.org/10.1155/2024/5374764 |
| spellingShingle | Qing Yang Fujun Ji Fei Liu An Efficient and Differential Privacy-Based Scheme for Aggregating Mobility Datasets Journal of Advanced Transportation |
| title | An Efficient and Differential Privacy-Based Scheme for Aggregating Mobility Datasets |
| title_full | An Efficient and Differential Privacy-Based Scheme for Aggregating Mobility Datasets |
| title_fullStr | An Efficient and Differential Privacy-Based Scheme for Aggregating Mobility Datasets |
| title_full_unstemmed | An Efficient and Differential Privacy-Based Scheme for Aggregating Mobility Datasets |
| title_short | An Efficient and Differential Privacy-Based Scheme for Aggregating Mobility Datasets |
| title_sort | efficient and differential privacy based scheme for aggregating mobility datasets |
| url | http://dx.doi.org/10.1155/2024/5374764 |
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