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“学习隐私”——不可忽视的数学课程资源
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作者 张亚敏 《小学教学研究》 2020年第21期79-81,共3页
"学习隐私"是指学生在学习过程中存有故意或无意地隐藏自己的学习想法、观点的情况。"学习隐私"具有潜在性、生成性和短暂性等特质。在小学数学教学中,教师要关注、保护、放大学生的"学习隐私",让学生的&... "学习隐私"是指学生在学习过程中存有故意或无意地隐藏自己的学习想法、观点的情况。"学习隐私"具有潜在性、生成性和短暂性等特质。在小学数学教学中,教师要关注、保护、放大学生的"学习隐私",让学生的"学习隐私"呈现预兆,让学生的"学习隐私"不再沉默,让学生的"学习隐私"绽放异彩。通过教师的激活、唤醒、弘扬,让学生的"学习隐私"成为学生最为重要的数学学习课程资源。 展开更多
关键词 小学数学 “学习隐私” 课程资源
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A Fast Federated Learning-based Crypto-aggregation Scheme and Its Simulation Analysis
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作者 LüBoshen Song Xiao 《系统仿真学报》 CAS CSCD 北大核心 2024年第12期2850-2870,共21页
To solve the problem of increased computation and communication costs caused by using homomorphic encryption(HE) to protect all gradients in traditional cryptographic aggregation(cryptoaggregation) schemes,a fast cryp... To solve the problem of increased computation and communication costs caused by using homomorphic encryption(HE) to protect all gradients in traditional cryptographic aggregation(cryptoaggregation) schemes,a fast crypto-aggregation scheme called RandomCrypt was proposed.RandomCrypt performed clipping and quantization to fix the range of gradient values and then added two types of noise on the gradient for encryption and differential privacy(DP) protection.It conducted HE on noise keys to revise the precision loss caused by DP protection.RandomCrypt was implemented based on a FATE framework,and a hacking simulation experiment was conducted.The results show that the proposed scheme can effectively hinder inference attacks while ensuring training accuracy.It only requires 45%~51% communication cost and 5%~23% computation cost compared with traditional schemes. 展开更多
关键词 federated learning differential privacy homomorphic encryption inference attack hacking simulation
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