| Autor: |
Shi, Zhenkui, Fu, Xuemei, Li, Xianxian, Zhu, Kai |
| Předmět: |
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| Zdroj: |
IEEE Transactions on Knowledge & Data Engineering; Jul2022, Vol. 34 Issue 7, p3241-3254, 14p |
| Abstrakt: |
Symmetric Searchable Encryption(SSE) is deemed to tackle the privacy issue as well as the operability and confidentiality in data outsourcing. However, most SSE schemes assume that the cloud is honest but curious. This assumption is not always applicable. And even if some schemes supported verification, integrity or freshness checking in a malicious cloud, but the performance and security functionalities are not fully exploited. In this paper, we propose an efficient SSE scheme based on B+-Tree and Counting Bloom Filter (CBF) which supports secure verification, dynamic updating, and multi-user queries. Comparing with the previous state of the arts, we design the new data structure CBF to support dynamic updating and boost verification. We also leverage the timestamp mechanism in the scheme to prevent the malicious cloud from launching a replay attack. The new designed CBF is like a front-engine to save user ${^{\prime }}$ ' s cost for query and verification. And it can achieve more efficient query and verification with negligible false positive when there is no value matching the queried keyword. The CBF supports efficient dynamic updating by combining Bloom Filter with a one-dimensional array that provides the counting capability. Furthermore, we design the authenticator for CBF. We adopt B+-Tree for it is widely used in many database engines and file systems. We also give a brief security proof of our scheme. Then we provide a detailed performance analysis. Finally, we evaluate our scheme through comprehensive experiments. The results are consistent with our analysis and show that our scheme is secure, and more efficient compared with the previous schemes with the same functionalities. The average performance can be improved by about 20 percent for both the cloud servers and users when the missing rate of the searching keywords is 20 percent. And the higher the missing rate is, the more the performance can be improved. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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