Pricing Personal Data Based on Data Provenance
Data have become an important asset. Mining the value contained in personal data, making personal data an exchangeable commodity, has become a hot spot of industry research. Then, how to price personal data reasonably becomes a problem we have to face. Based on previous research on data provenance,...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-08-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/9/16/3388 |
| _version_ | 1818024268337774592 |
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| author | Yuncheng Shen Bing Guo Yan Shen Fan Wu Hong Zhang Xuliang Duan Xiangqian Dong |
| author_facet | Yuncheng Shen Bing Guo Yan Shen Fan Wu Hong Zhang Xuliang Duan Xiangqian Dong |
| author_sort | Yuncheng Shen |
| collection | DOAJ |
| description | Data have become an important asset. Mining the value contained in personal data, making personal data an exchangeable commodity, has become a hot spot of industry research. Then, how to price personal data reasonably becomes a problem we have to face. Based on previous research on data provenance, this paper proposes a novel minimum provenance pricing method, which is to price the minimum source tuple set that contributes to the query. Our pricing model first sets prices for source tuples according to their importance and then makes query pricing based on data provenance, which considers both the importance of the data itself and the relationships between the data. We design an exact algorithm that can calculate the exact price of a query in exponential complexity. Furthermore, we design an easy approximate algorithm, which can calculate the approximate price of the query in polynomial time. We instantiated our model with a select-joint query and a complex query and extensively evaluated its performances on two practical datasets. The experimental results show that our pricing model is feasible. |
| first_indexed | 2024-12-10T03:57:31Z |
| format | Article |
| id | doaj.art-031d2d9e1f914e3c81d1f00a55b591eb |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-12-10T03:57:31Z |
| publishDate | 2019-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-031d2d9e1f914e3c81d1f00a55b591eb2022-12-22T02:03:03ZengMDPI AGApplied Sciences2076-34172019-08-01916338810.3390/app9163388app9163388Pricing Personal Data Based on Data ProvenanceYuncheng Shen0Bing Guo1Yan Shen2Fan Wu3Hong Zhang4Xuliang Duan5Xiangqian Dong6College of Computer Science, Sichuan University, Chengdu 610065, ChinaCollege of Computer Science, Sichuan University, Chengdu 610065, ChinaSchool of Control Engineering, Chengdu University of Information Technology, Chengdu 610225, ChinaShanghai Key Laboratory of Scalable Computing and Systems, Shanghai Jiao Tong University, Shanghai 200240, ChinaCollege of Computer Science, Sichuan University, Chengdu 610065, ChinaCollege of Computer Science, Sichuan University, Chengdu 610065, ChinaCollege of Computer Science, Sichuan University, Chengdu 610065, ChinaData have become an important asset. Mining the value contained in personal data, making personal data an exchangeable commodity, has become a hot spot of industry research. Then, how to price personal data reasonably becomes a problem we have to face. Based on previous research on data provenance, this paper proposes a novel minimum provenance pricing method, which is to price the minimum source tuple set that contributes to the query. Our pricing model first sets prices for source tuples according to their importance and then makes query pricing based on data provenance, which considers both the importance of the data itself and the relationships between the data. We design an exact algorithm that can calculate the exact price of a query in exponential complexity. Furthermore, we design an easy approximate algorithm, which can calculate the approximate price of the query in polynomial time. We instantiated our model with a select-joint query and a complex query and extensively evaluated its performances on two practical datasets. The experimental results show that our pricing model is feasible.https://www.mdpi.com/2076-3417/9/16/3388personal datadata provenancearbitragedata pricing |
| spellingShingle | Yuncheng Shen Bing Guo Yan Shen Fan Wu Hong Zhang Xuliang Duan Xiangqian Dong Pricing Personal Data Based on Data Provenance Applied Sciences personal data data provenance arbitrage data pricing |
| title | Pricing Personal Data Based on Data Provenance |
| title_full | Pricing Personal Data Based on Data Provenance |
| title_fullStr | Pricing Personal Data Based on Data Provenance |
| title_full_unstemmed | Pricing Personal Data Based on Data Provenance |
| title_short | Pricing Personal Data Based on Data Provenance |
| title_sort | pricing personal data based on data provenance |
| topic | personal data data provenance arbitrage data pricing |
| url | https://www.mdpi.com/2076-3417/9/16/3388 |
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