Paper Info
Title
MPLDS: An integration of CP-ABE and local differential privacy for achieving multiple privacy levels data sharing
Abstract
In ciphertext-policy attribute-based encryption (CP-ABE), once malicious users successfully decrypt the encrypted data, they can obtain the real original personal privacy data, leading to serious privacy leakages problems. Thus, if the user does not access the original private data but the perturbed data while guaranteeing statistical characteristics, the privacy protection capabilities of CP-ABE will be greatly improved. Motivated by this, an integration of basic CP-ABE and local differential privacy (LDP) or achieving multiple privacy levels data sharing (MPLDS) is constructed to provide double privacy protection for data owners, which is with a relatively lower complexity and higher data utility. To prevent different trusted users from colluding and gaining more privacy beyond their trust levels, a randomized perturbation strategy is elaborately designed for resisting collusion attacks (RCA) while ensuring the fact that the output of RCA perturbation strategy is the same as that of the original perturbation, which has been proved from the theoretical level. Finally, the proposed MPLDS scheme is simulated and verified on both synthetic and real data sets, which indicates that the proposed MPLDS scheme outperforms the existing MPPDS scheme while greatly reducing the complexity.
Year
DOI
Venue
2022
10.1007/s12083-021-01238-8
PEER-TO-PEER NETWORKING AND APPLICATIONS
Keywords
DocType
Volume
Privacy preservation, Multiple privacy levels, Ciphertext-policy attribute-based encryption (CP-ABE), Local privacy differential (LDP), Resisting collusion attacks
Journal
15
Issue
ISSN
Citations 
1
1936-6442
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Haina Song111.70
Xinyu Han200.68
Jie Lv300.34
Tao Luo400.68
Jianfeng Li500.68