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Sandbox AI: We Don't Trust Each Other but Want to Create New Value Efficiently Through Collaboration Using Sensitive Data., , , , and . UbiComp/ISWC Adjunct, page 68-72. ACM, (2023)MOTUS: How Quantized Parameters Improve Protection of Model and Its Inference Input., , , , and . SecITC, volume 13809 of Lecture Notes in Computer Science, page 184-202. Springer, (2022)SPGC: An Integrated Framework of Secure Computation and Differential Privacy for Collaborative Learning., , , and . DPM/CBT@ESORICS, volume 13140 of Lecture Notes in Computer Science, page 89-105. Springer, (2021)SPGC: Integration of Secure Multiparty Computation and Differential Privacy for Gradient Computation on Collaborative Learning., , , and . J. Inf. Process., (2022)A Study on Quantized Parameters for Protection of a Model and Its Inference Input., , , , and . J. Inf. Process., (2023)Can Inputs' Reconstruction Information Be Coded into Machine Learning Model's Outputs?, , , and . ESORICS Workshops (2), volume 14399 of Lecture Notes in Computer Science, page 665-682. Springer, (2023)MADMAX: Browser-Based Malicious Domain Detection Through Extreme Learning Machine., , , , , , , , , and . IEEE Access, (2021)First to Possess His Statistics: Data-Free Model Extraction Attack on Tabular Data., , , , , , , and . CoRR, (2021)Backdoor Attacks Leveraging Latent Representation in Competitive Learning., , and . ESORICS Workshops (2), volume 14399 of Lecture Notes in Computer Science, page 700-718. Springer, (2023)