PLDP: Personalized Local Differential Privacy for Multidimensional Data Aggregation
The collection of multidimensional crowdsourced data has caused a public concern because of the privacy issues. To address it, local differential privacy (LDP) is proposed to protect the crowdsourced data without much loss of usage, which is popularly used in practice. However, the existing LDP prot...
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| Veröffentlicht in: | Security and communication networks 2021-01, Vol.2021, p.1-13 |
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| creator | Shen, Zixuan Xia, Zhihua Yu, Peipeng |
| description | The collection of multidimensional crowdsourced data has caused a public concern because of the privacy issues. To address it, local differential privacy (LDP) is proposed to protect the crowdsourced data without much loss of usage, which is popularly used in practice. However, the existing LDP protocols ignore users’ personal privacy requirements in spite of offering good utility for multidimensional crowdsourced data. In this paper, we consider the personality of data owners in protection and utilization of their multidimensional data by introducing the notion of personalized LDP (PLDP). Specifically, we design personalized multiple optimized unary encoding (PMOUE) to perturb data owners’ data, which satisfies ϵtotal-PLDP. Then, the aggregation algorithm for frequency estimation on multidimensional data under PLDP is developed, which is described in two situations. Experiments are conducted on four real datasets, and the results show that the proposed aggregation algorithm yields high utility. Moreover, case studies with four real datasets demonstrate the efficiency and superiority of the proposed scheme. |
| doi_str_mv | 10.1155/2021/6684179 |
| format | Article |
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To address it, local differential privacy (LDP) is proposed to protect the crowdsourced data without much loss of usage, which is popularly used in practice. However, the existing LDP protocols ignore users’ personal privacy requirements in spite of offering good utility for multidimensional crowdsourced data. In this paper, we consider the personality of data owners in protection and utilization of their multidimensional data by introducing the notion of personalized LDP (PLDP). Specifically, we design personalized multiple optimized unary encoding (PMOUE) to perturb data owners’ data, which satisfies ϵtotal-PLDP. Then, the aggregation algorithm for frequency estimation on multidimensional data under PLDP is developed, which is described in two situations. Experiments are conducted on four real datasets, and the results show that the proposed aggregation algorithm yields high utility. 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| source | Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Wiley-Blackwell Open Access Titles; Alma/SFX Local Collection |
| subjects | Agglomeration Algorithms Big Data Budgets Crowdsourcing Customization Data management Datasets Design Design optimization Multidimensional data Privacy User requirements |
| title | PLDP: Personalized Local Differential Privacy for Multidimensional Data Aggregation |
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