Efficient privacy-preserving data merging and skyline computation over multi-source encrypted data
Efficient privacy-preserving data merging and skyline computation over multi-source encrypted data
复制标题
多源加密数据的高效隐私保护数据合并和天际线计算
DOI:
10.1016/j.ins.2019.05.055
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发表时间:
2019-09
影响因子:
8.1
通讯作者:
Kim-Kwang Raymond Choo
中科院分区:
文献类型:
--
作者:
Y;ong Zheng;Rongxing Lu;Beibei Li;Jun Shao;Haomiao Yang;Kim-Kwang Raymond Choo
Efficient data merging from the significant amount of data routinely collected from various data sources is crucial in the uncovering of relevant and key information of interest (eg, skyline). There are, however, privacy considerations during data merging and skyline operations, particularly when dealing with sensitive data (eg, healthcare data). Existing focuses on data merging and skyline computation either do not (fully) consider data privacy or have low efficiency. Thus, in this paper, we aim to address both privacy and efficiency during data merging and skyline computations over multi-source encrypted data. Specifically, we integrate the leftist tree with public key encryption and index based skyline computation to achieve data merging and skyline computation over encrypted data. First, we design a non-interactive data comparison protocol using public key encryption technique. This allows us to compare encrypted and outsourced data under a single cloud server instead of two non-colluding cloud servers in previous studies. Then, we combine the leftist tree with public key encryption to achieve privacy-preserving data merging with high efficiency, namely, O (log 2 (n 1+ n 2)) computational complexity for merging two leftist trees of sizes n 1 and n 2. Third, we present an index and leftist tree based skyline computation algorithm, which can efficiently perform skyline query over the merged encrypted data. Then, detailed security analysis and performance evaluation demonstrate that our scheme is both secure and efficient for data merging and skyline computation.
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DOI:
--
发表时间:
1972
期刊:
--
影响因子:
--
作者:
Clark A. Crane
通讯作者:
Clark A. Crane
影响因子:
10.6
作者:
Hua, Jiafeng;Zhu, Hui;Zhang, Yeping
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Zhang, Yeping
DOI:
10.1016/b978-155860869-6/50032-9
发表时间:
2002-08
期刊:
--
影响因子:
--
作者:
Donald Kossmann;Frank Ramsak;S. Rost
通讯作者:
Donald Kossmann;Frank Ramsak;S. Rost
DOI:
10.1109/icde.2007.367887
发表时间:
2007-04
期刊:
2007 IEEE 23rd International Conference on Data Engineering
影响因子:
--
作者:
A. Vlachou;C. Doulkeridis;Y. Kotidis;M. Vazirgiannis
通讯作者:
A. Vlachou;C. Doulkeridis;Y. Kotidis;M. Vazirgiannis
DOI:
10.1109/icde.2007.368971
发表时间:
2007-04
期刊:
2007 IEEE 23rd International Conference on Data Engineering
影响因子:
--
作者:
Shiyuan Wang;B. Ooi;A. Tung;Lizhen Xu
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Shiyuan Wang;B. Ooi;A. Tung;Lizhen Xu