Abstract
With the advent of big data era, clients lack of computational resources tend to outsource their data and mining tasks to resourceful cloud service providers. Generally, the outsourced data contributed by multiple clients should be encrypted under multiple keys for privacy and security concerns. Unfortunately, existing secure outsourcing protocols are either restricted to a single key or quite inefficient due to frequent client interactions, making the deployment far from practical. In this paper, we focus on addressing these outsourced problems over encrypted data under multiple keys, and propose an efficient Outsourced Privacy-Preserving Scalar Product (OPPSP) protocol. Theoretical analysis shows that the proposed solution preserves data confidentiality of all participating users in the semi-honest model with negligible computation and communication costs. Experimental evaluation also demonstrates its practicability and efficiency.
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Rong, H., Wang, H., Huang, K., Liu, J., Xian, M. (2016). Privacy-Preserving Scalar Product Computation in Cloud Environments Under Multiple Keys. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2016. IDEAL 2016. Lecture Notes in Computer Science(), vol 9937. Springer, Cham. https://doi.org/10.1007/978-3-319-46257-8_27
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DOI: https://doi.org/10.1007/978-3-319-46257-8_27
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