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Privacy-Preserving Joint Data and Function Homomorphic Encryption for Cloud Software Services
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AI
DOI：10.1109/JIOT.2023.3286508.png)
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With the widespread growth of cloud computing technology, cloud software services are ubiquitous these days. Using this technology, software providers can sell their products through cloud computing environments in the pay-as-you-use fashion. However, performing secure and accurate calculations in cloud computing environments has become extremely challenging. As the data to be processed by cloud software might be highly sensitive, its confidentiality needs to be taken care of before transferring the data to the cloud server. Also, in addition to the data confidentiality, the security of algorithms employed in the software is of vital importance, and thus software owners may be worried about revealing their algorithms through the cloud server. Homomorphic cryptosystems can provide confidentiality for data to be processed online. However, the confidentiality of algorithms is still an open problem. To address this issue, we put forward a privacy-preserving joint data and function homomorphic encryption (JDF-HE) mechanism. Our JDF-HE can provide confidentiality for both algorithms and data, thereby being suitable for cloud software services. We prove the security of JDF-HE and analyze its performance by evaluating its actual execution overhead. Our performance and security analysis demonstrate that JDF-HE is secure and suitable for real-time applications.
Keyword:
Cloud computing
cloud software
data confi-dentiality
homomorphic cryptosystems
privacy preserving. to tioned
期刊
IF:
8.9
论文数:
1.4W
被引数:
7.8W
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