Akademik Veri Yönetim Sistemi
2023 IEEE International Conference on Smart Mobility, SM 2023, Thuwal, Suudi Arabistan, 19 - 21 Mart 2023, ss.51-58
A server has an already trained decision tree machine learning model and one or more clients have unclassified query(ies) that they wish to classify using the server's model under strict security, privacy, and efficiency constraints. To do so, already existing secure building blocks are used, improved, and adjusted to fit this scenario. On top of the proposed building blocks, novel secure and private Decision Tree Evaluation (sDTE) algorithms are proposed. The proposed building blocks show better performances than the related ones in literature in terms of computation and communication costs. Consequently, experimental evaluations over benchmark datasets show that the proposed sDTE algorithms build on top of the proposed blocks, also outperform the state-of-the-art ones in terms of computation and communication costs as well as on security and privacy characteristics. Our theoretical analysis shows that if the whole decision tree can fit in a single ciphertext, which in the proposed sDTE algorithms is almost always the case, then private tree evaluations are done in constant time and do not depend on the tree depth. To the best of the author's knowledge, this is the first scheme in literature with such properties.