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- W3045857513 abstract "Decision trees are prevalent machine learning models used for data classification. Cloud servers can build their decision tree models and provide users with many classification services, such as remote medical diagnosis. Moreover, users would also like to share the classification results with third-party applications for customized services. For example, the medical diagnosis results can be further utilized by a nutrition application to provide users with dietary recommendations. However, as stringent privacy regulations of personal data, such as GDPR, takes effect, the decision tree evaluation must comply with the following requirements. First, the model parameters and user data (input and output) should be protected from public disclosure. Second, different applications should obtain the classification results with users' consent in the context of user-customised services. In this paper, we propose a consent-based privacy-preserving decision tree evaluation scheme, named CPDE. Specifically, to achieve model parameter privacy and user data privacy, the original decision tree evaluation is conducted in a private manner in CPDE. As a result, all operations can be performed in the encrypted domain using an additively homomorphic encryption primitive and a secure comparison protocol. In addition, by integrating a proxy re-encryption technique, CDPE enables user-authorized applications to obtain the user's classification results even if the user is offline. The security analysis shows that CPDE achieves the desirable security properties and performance evaluation demonstrates CPDE is efficient and is suitable for real-world implementations." @default.
- W3045857513 created "2020-08-03" @default.
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- W3045857513 date "2020-06-01" @default.
- W3045857513 modified "2023-09-27" @default.
- W3045857513 title "Consent-based Privacy-preserving Decision Tree Evaluation" @default.
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- W3045857513 doi "https://doi.org/10.1109/icc40277.2020.9149181" @default.
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