Key-value data collection and statistical analysis with local differential privacy
The collection and statistical analysis of simple data types (e.g., categorical, numerical and multi-dimensional data) under local differential privacy has been widely studied. Recently, researchers have focused on the collection of the key-value data, which is one of the main types of NoSQL data mo...
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| Vydané v: | Information sciences Ročník 640; s. 119058 |
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| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier Inc
01.09.2023
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| ISSN: | 0020-0255, 1872-6291 |
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| Abstract | The collection and statistical analysis of simple data types (e.g., categorical, numerical and multi-dimensional data) under local differential privacy has been widely studied. Recently, researchers have focused on the collection of the key-value data, which is one of the main types of NoSQL data model. In the collection and statistical analysis of key-value data under local differential privacy, the frequency and mean of each key must be estimated simultaneously. However, achieving a good utility-privacy tradeoff is difficult, because key-value data has inherent correlation, and some users may have different numbers of key-value pairs. In this paper, we propose an efficient sampling based scheme for collecting and analyzing key-value data. Note that the more valid data collected, the higher the accuracy of statistical data under the same disturbance level and disturbance algorithm. Therefore, we make full use of probability sampling and the inherent correlation of key-value data to improve the probability of users submitting valid key-value data. Moreover, we optimize the budget allocation on key-value data, so that the overall variance of frequency and mean estimation is close to optimal. Detailed theoretical analysis and experimental results show that the proposed scheme is superior to existing schemes in accuracy.
•We propose an efficient SKV-GRR scheme with separate key and value selection for collecting and analyzing key-value data.•In the key selection, we use unequal probability sampling to improve the probability of users submitting valid data.•The value selection based on weak correlated perturbation can improve the probability of users submitting valid value data.•We optimize the budget allocation on the selected key and the selected value to improve the accuracy of estimated data. |
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| AbstractList | The collection and statistical analysis of simple data types (e.g., categorical, numerical and multi-dimensional data) under local differential privacy has been widely studied. Recently, researchers have focused on the collection of the key-value data, which is one of the main types of NoSQL data model. In the collection and statistical analysis of key-value data under local differential privacy, the frequency and mean of each key must be estimated simultaneously. However, achieving a good utility-privacy tradeoff is difficult, because key-value data has inherent correlation, and some users may have different numbers of key-value pairs. In this paper, we propose an efficient sampling based scheme for collecting and analyzing key-value data. Note that the more valid data collected, the higher the accuracy of statistical data under the same disturbance level and disturbance algorithm. Therefore, we make full use of probability sampling and the inherent correlation of key-value data to improve the probability of users submitting valid key-value data. Moreover, we optimize the budget allocation on key-value data, so that the overall variance of frequency and mean estimation is close to optimal. Detailed theoretical analysis and experimental results show that the proposed scheme is superior to existing schemes in accuracy.
•We propose an efficient SKV-GRR scheme with separate key and value selection for collecting and analyzing key-value data.•In the key selection, we use unequal probability sampling to improve the probability of users submitting valid data.•The value selection based on weak correlated perturbation can improve the probability of users submitting valid value data.•We optimize the budget allocation on the selected key and the selected value to improve the accuracy of estimated data. |
| ArticleNumber | 119058 |
| Author | Peng, Shuangrong Tang, Xiaohu Zhu, Hui Fu, Chao Yang, Laurence Tianruo |
| Author_xml | – sequence: 1 givenname: Hui surname: Zhu fullname: Zhu, Hui organization: The School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China – sequence: 2 givenname: Xiaohu surname: Tang fullname: Tang, Xiaohu email: xhutang@swjtu.edu.cn organization: The School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China – sequence: 3 givenname: Laurence Tianruo orcidid: 0000-0002-7986-4244 surname: Yang fullname: Yang, Laurence Tianruo organization: School of Computer Science and Technology, Hainan University, Haikou, China – sequence: 4 givenname: Chao surname: Fu fullname: Fu, Chao organization: School of Mathematics, Southwest Jiaotong University, Chengdu, China – sequence: 5 givenname: Shuangrong surname: Peng fullname: Peng, Shuangrong organization: The School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China |
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| Cites_doi | 10.1109/TMC.2020.3003673 10.14778/3407790.3407798 10.1080/01621459.1965.10480775 10.1007/s11432-018-9849-y 10.14778/3547305.3547312 10.1109/TDSC.2019.2927695 10.14778/3430915.3430927 10.1109/TIFS.2018.2812146 10.1109/TIFS.2022.3198283 10.1007/s11432-022-3583-x 10.1016/j.knosys.2022.110213 10.1109/TKDE.2020.3047124 |
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| Keywords | Local differential privacy Frequency estimation Mean estimation Key-value data |
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Knowl. Data Eng. doi: 10.1109/TKDE.2020.3047124 – start-page: 1832 year: 2019 ident: 10.1016/j.ins.2023.119058_br0090 article-title: Answering range queries under local differential privacy – start-page: 127 year: 2015 ident: 10.1016/j.ins.2023.119058_br0040 article-title: Local, private, efficient protocols for succinct histograms – start-page: 159 year: 2019 ident: 10.1016/j.ins.2023.119058_br0100 article-title: Answering multi-dimensional analytical queries under local differential privacy – start-page: 729 year: 2017 ident: 10.1016/j.ins.2023.119058_br0050 article-title: Locally differentially private protocols for frequency estimation |
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