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• W1544333246 abstract "Individuals and companies, taking advantage of cloud computing which affords both resource and compute scalability, are willing to outsource their exploding data to save the storage and managing cost, however, users often do not fully trust the cloud and therefore outsource their private data after encryption to protect the data privacy. Here, as the data are both encrypted and outsourced in the cloud, how to securely and efficiently store and process such data becomes a challenging task and a primary concern. Support vector machine (SVM) classification, among different data mining and machine learning algorithms, has been very widely used in practical applications, which however, does not have a corresponding solution for such outsourced and encrypted data. Also, existing secure methods only assume that the data is locally stored by users rather than outsourced. To address this problem, we propose a novel Protocol for Outsourced SVM (POS) in this paper. POS lets cloud and users perform collaborative operations on encrypted and outsourced data without violating the data privacy contributed by each user. We formally verified that POS is correct and secure. We also conducted experimental analysis." @default.
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• W1544333246 date "2015-06-01" @default.
• W1544333246 modified "2023-09-27" @default.
• W1544333246 title "Encrypted SVM for Outsourced Data Mining" @default.
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• W1544333246 doi "https://doi.org/10.1109/cloud.2015.158" @default.
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