The Inner Product Masking(IPM)scheme has been shown to provide higher theoretical security guarantees than the BooleanMasking(BM).This scheme aims to increase the algebraic complexity of the coding to achieve a higher...The Inner Product Masking(IPM)scheme has been shown to provide higher theoretical security guarantees than the BooleanMasking(BM).This scheme aims to increase the algebraic complexity of the coding to achieve a higher level of security.Some previous work unfolds when certain(adversarial and implementation)conditions are met,and we seek to complement these investigations by understanding what happens when these conditions deviate from their expected behaviour.In this paper,we investigate the security characteristics of IPM under different conditions.In adversarial condition,the security properties of first-order IPMs obtained through parametric characterization are preserved in the face of univariate and bivariate attacks.In implementation condition,we construct two new polynomial leakage functions to observe the nonlinear leakage of the IPM and connect the security order amplification to the nonlinear function.We observe that the security of IPMis affected by the degree and the linear component in the leakage function.In addition,the comparison experiments from the coefficients,signal-to-noise ratio(SNR)and the public parameter show that the security properties of the IPM are highly implementation-dependent.展开更多
With the increasing awareness of privacy protection and the improvement of relevant laws,federal learning has gradually become a new choice for cross-agency and cross-device machine learning.In order to solve the prob...With the increasing awareness of privacy protection and the improvement of relevant laws,federal learning has gradually become a new choice for cross-agency and cross-device machine learning.In order to solve the problems of privacy leakage,high computational overhead and high traffic in some federated learning schemes,this paper proposes amultiplicative double privacymask algorithm which is convenient for homomorphic addition aggregation.The combination of homomorphic encryption and secret sharing ensures that the server cannot compromise user privacy from the private gradient uploaded by the participants.At the same time,the proposed TQRR(Top-Q-Random-R)gradient selection algorithm is used to filter the gradient of encryption and upload efficiently,which reduces the computing overhead of 51.78%and the traffic of 64.87%on the premise of ensuring the accuracy of themodel,whichmakes the framework of privacy protection federated learning lighter to adapt to more miniaturized federated learning terminals.展开更多
背景:3D打印技术可根据患者实际病情和治疗需求设计构建模型、手术导板和个性化植入体或固定物,在创伤性骨折修复中展示了巨大的应用前景。目的:综述3D打印技术在创伤性骨折中的应用。方法:检索Web of science、PubMed和中国知网数据库2...背景:3D打印技术可根据患者实际病情和治疗需求设计构建模型、手术导板和个性化植入体或固定物,在创伤性骨折修复中展示了巨大的应用前景。目的:综述3D打印技术在创伤性骨折中的应用。方法:检索Web of science、PubMed和中国知网数据库2020-2024年发表的创伤骨科领域3D打印技术应用的相关文献,英文检索词为“traumatic fracture,3D printing technology,digital model,surgical guide”,中文检索词为“创伤性骨折,3D打印技术,数字模型,手术导板”,经筛选和分析,最终纳入60篇文献进行分析。结果与结论:①创伤性骨折是各种致伤因素导致的骨骼连续性中断和完整性破坏的骨折现象,以可靠方案提高复位愈合效果,已成为骨外科相关研究领域亟需解决的热点问题;②3D打印技术是以数字模型数据为基础的,运用粉末状金属或聚合物等可黏合成型材料以立体光刻、沉积建模和光聚合物喷射等形式制造满足需求三维实体的技术,在数字骨科生物医学领域应用广泛;③3D打印技术在疾病诊断、术前规划、重建骨折三维模型、定制骨科植入体、定制固定支具及假肢、手术导板制作和骨缺损修复等方面发挥了显著的优势,可根据患者实际病情和治疗需求设计构建模型、手术导板和个性化植入体或固定物,为创伤性骨折的治疗提供了新的思路。展开更多
基金the Hunan Provincial Natrual Science Foundation of China(2022JJ30103)“the 14th Five-Year”Key Disciplines and Application Oriented Special Disciplines of Hunan Province(Xiangjiaotong[2022]351)the Science and Technology Innovation Program of Hunan Province(2016TP1020).
文摘The Inner Product Masking(IPM)scheme has been shown to provide higher theoretical security guarantees than the BooleanMasking(BM).This scheme aims to increase the algebraic complexity of the coding to achieve a higher level of security.Some previous work unfolds when certain(adversarial and implementation)conditions are met,and we seek to complement these investigations by understanding what happens when these conditions deviate from their expected behaviour.In this paper,we investigate the security characteristics of IPM under different conditions.In adversarial condition,the security properties of first-order IPMs obtained through parametric characterization are preserved in the face of univariate and bivariate attacks.In implementation condition,we construct two new polynomial leakage functions to observe the nonlinear leakage of the IPM and connect the security order amplification to the nonlinear function.We observe that the security of IPMis affected by the degree and the linear component in the leakage function.In addition,the comparison experiments from the coefficients,signal-to-noise ratio(SNR)and the public parameter show that the security properties of the IPM are highly implementation-dependent.
基金supported by the National Natural Science Foundation of China(Grant Nos.62172436,62102452)the National Key Research and Development Program of China(2023YFB3106100,2021YFB3100100)the Natural Science Foundation of Shaanxi Province(2023-JC-YB-584).
文摘With the increasing awareness of privacy protection and the improvement of relevant laws,federal learning has gradually become a new choice for cross-agency and cross-device machine learning.In order to solve the problems of privacy leakage,high computational overhead and high traffic in some federated learning schemes,this paper proposes amultiplicative double privacymask algorithm which is convenient for homomorphic addition aggregation.The combination of homomorphic encryption and secret sharing ensures that the server cannot compromise user privacy from the private gradient uploaded by the participants.At the same time,the proposed TQRR(Top-Q-Random-R)gradient selection algorithm is used to filter the gradient of encryption and upload efficiently,which reduces the computing overhead of 51.78%and the traffic of 64.87%on the premise of ensuring the accuracy of themodel,whichmakes the framework of privacy protection federated learning lighter to adapt to more miniaturized federated learning terminals.
