Privacy-protected crowd-sensed data trading algorithm
To solve the problem that data privacy leakage of participants under the crowd-sensed data trading model, a privacy-protected crowd-sensed data trading algorithm was proposed.Firstly, to achieve the privacy protection of participants, an aggregation scheme based on differential privacy was designed....
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2022-05-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2022082/ |
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| _version_ | 1841540006099812352 |
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| author | Yong ZHANG Dandan LI Lu HAN Xiaohong HUANG |
| author_facet | Yong ZHANG Dandan LI Lu HAN Xiaohong HUANG |
| author_sort | Yong ZHANG |
| collection | DOAJ |
| description | To solve the problem that data privacy leakage of participants under the crowd-sensed data trading model, a privacy-protected crowd-sensed data trading algorithm was proposed.Firstly, to achieve the privacy protection of participants, an aggregation scheme based on differential privacy was designed.Participants were no longer needed to upload raw data, but analyzed and calculated the collected data according to the task requirements, and then sent the analysis results to the platform after adding noise in accordance with the privacy budget allocated by the platform to protect their privacy.Secondly, in order to ensure the credibility of participants, a reputation model of participants was proposed.Finally, in order to encourage consumers and participants to participate in transactions, a data trading optimization model was constructed by considering the consumer’s constraint on the result deviation，the participant’s privacy leakage compensation and platform profit, and a POA based on genetic algorithm was proposed to solve the model.The simulation results show that the POA not only protects the privacy of participants, but also increases the profit of the platform by 29.27% and 20.45% compared to VENUS and DPDT, respectively. |
| format | Article |
| id | doaj-art-f9a7afd13ff2403f99d675a07000f933 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2022-05-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-f9a7afd13ff2403f99d675a07000f9332025-01-14T06:29:48ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2022-05-014311359395359Privacy-protected crowd-sensed data trading algorithmYong ZHANGDandan LILu HANXiaohong HUANGTo solve the problem that data privacy leakage of participants under the crowd-sensed data trading model, a privacy-protected crowd-sensed data trading algorithm was proposed.Firstly, to achieve the privacy protection of participants, an aggregation scheme based on differential privacy was designed.Participants were no longer needed to upload raw data, but analyzed and calculated the collected data according to the task requirements, and then sent the analysis results to the platform after adding noise in accordance with the privacy budget allocated by the platform to protect their privacy.Secondly, in order to ensure the credibility of participants, a reputation model of participants was proposed.Finally, in order to encourage consumers and participants to participate in transactions, a data trading optimization model was constructed by considering the consumer’s constraint on the result deviation，the participant’s privacy leakage compensation and platform profit, and a POA based on genetic algorithm was proposed to solve the model.The simulation results show that the POA not only protects the privacy of participants, but also increases the profit of the platform by 29.27% and 20.45% compared to VENUS and DPDT, respectively.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2022082/crowd sensingdata tradingdifferential privacyreputation model |
| spellingShingle | Yong ZHANG Dandan LI Lu HAN Xiaohong HUANG Privacy-protected crowd-sensed data trading algorithm Tongxin xuebao crowd sensing data trading differential privacy reputation model |
| title | Privacy-protected crowd-sensed data trading algorithm |
| title_full | Privacy-protected crowd-sensed data trading algorithm |
| title_fullStr | Privacy-protected crowd-sensed data trading algorithm |
| title_full_unstemmed | Privacy-protected crowd-sensed data trading algorithm |
| title_short | Privacy-protected crowd-sensed data trading algorithm |
| title_sort | privacy protected crowd sensed data trading algorithm |
| topic | crowd sensing data trading differential privacy reputation model |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2022082/ |
| work_keys_str_mv | AT yongzhang privacyprotectedcrowdsenseddatatradingalgorithm AT dandanli privacyprotectedcrowdsenseddatatradingalgorithm AT luhan privacyprotectedcrowdsenseddatatradingalgorithm AT xiaohonghuang privacyprotectedcrowdsenseddatatradingalgorithm |