Titlebook: Network and System Security; 17th International C Shujun Li,Mark Manulis,Atsuko Miyaji Conference proceedings 2023 The Editor(s) (if applic

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RRML: Privacy Preserving Machine Learning Based on?Random Response Technologys applications in sensitive scenarios. Typically, the state-of-the-art privacy preserving machine learning methods tend to provide privacy protection guarantee at the cost of losing data utility. This inevitably causes degradation of the model performance, especially for those tasks who are trained

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ppAURORA: Privacy Preserving Area Under Receiver Operating Characteristic and?Precision-Recall Curve of many research projects. Many of these methods are trained on privacy-sensitive data and there are several different approaches like .-differential privacy, federated learning and cryptography if the datasets cannot be shared or used jointly at one place for training and/or testing. In this setti

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Amirah Almutairi,BooJoong Kang,Nawfal Fadhelion of all the tools necessary for project work and running workshops when improving processes. All tools have been illustrated in a clear and comprehensible structure with examples and tips for applying the tools included. The chronology corresponds to the procedure of an improvement project compri

Maximizer

ion of all the tools necessary for project work and running workshops when improving processes. All tools have been illustrated in a clear and comprehensible structure with examples and tips for applying the tools included. The chronology corresponds to the procedure of an improvement project compri

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Xiangyu Su,Mario Larangeira,Keisuke TanakaProvides tools that support the practitioner in answering th.The current, second edition of this book reflects the 15 years of?practical experience with the Six Sigma+Lean toolbox. It is a comprehensive collection of all the tools necessary for project work and running workshops when improving proce

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