Digital signature,one of the most important cryptographic primitives,has been commonly used in information systems,and thus enhancing the security of a signature scheme can benefit such an application.Currently,leakag...Digital signature,one of the most important cryptographic primitives,has been commonly used in information systems,and thus enhancing the security of a signature scheme can benefit such an application.Currently,leakage-resilient cryptography is a very hot topic in cryptographic research.A leakage-resilient cryptographic primitive is said to be secure if arbitrary but bounded information about the signer's secret key(involving other states) is leaked to an adversary.Obviously,the leakage-resilient signature is more secure than the common signature.We construct an efficient leakage-resilient signature scheme based on BLS signature in the bounded retrieval model.We also prove that our scheme is provably secure under BLS signature.展开更多
The robotics industry has seen rapid development in recent years due to the Corona Virus Disease 2019.With the development of sensors and smart devices,factories and enterprises have accumulated a large amount of data...The robotics industry has seen rapid development in recent years due to the Corona Virus Disease 2019.With the development of sensors and smart devices,factories and enterprises have accumulated a large amount of data in their daily production,which creates extremely favorable conditions for robots to perform machine learning.However,in recent years,people’s awareness of data privacy has been increasing,leading to the inability to circulate data between different enterprises,resulting in the emergence of data silos.The emergence of federated learning provides a feasible solution to this problem,and the combination of federated learning and multi-robot systems can break down data silos and improve the overall performance of robots.However,as scholars have studied more deeply,they found that federated learning has very limited privacy protection.Therefore,how to protect data privacy from infringement remains an important issue.In this paper,we first give a brief introduction to the current development of multi-robot and federated learning;second,we review three aspects of privacy protection methods commonly used,privacy protection methods for multi-robot,and Other Problems Faced by Multi-robot Systems,focusing on method comparisons and challenges;and finally draw conclusions and predict possible future research directions.展开更多
基金supported by National Natural Science Foundation of China under Grant No.60703086,No.60973046Postdoctoral Science Foundation of China under Grant No. 20090451241Program for Talents in Nanjing University of Posts and Telecommunications under Grant No.NY209014
文摘Digital signature,one of the most important cryptographic primitives,has been commonly used in information systems,and thus enhancing the security of a signature scheme can benefit such an application.Currently,leakage-resilient cryptography is a very hot topic in cryptographic research.A leakage-resilient cryptographic primitive is said to be secure if arbitrary but bounded information about the signer's secret key(involving other states) is leaked to an adversary.Obviously,the leakage-resilient signature is more secure than the common signature.We construct an efficient leakage-resilient signature scheme based on BLS signature in the bounded retrieval model.We also prove that our scheme is provably secure under BLS signature.
基金the National Natural Science Foundation of China(No.62063006)to the Natural Science Foundation of Guangxi Province(No.2023GXNSFAA026025)+2 种基金to the Innovation Fund of Chinese Universities Industry-University-Research(ID:2021RYC06005)to the Research Project for Young and Middle-Aged Teachers in Guangxi Universities(ID:2020KY15013)to the Special Research Project of Hechi University(ID:2021GCC028).
文摘The robotics industry has seen rapid development in recent years due to the Corona Virus Disease 2019.With the development of sensors and smart devices,factories and enterprises have accumulated a large amount of data in their daily production,which creates extremely favorable conditions for robots to perform machine learning.However,in recent years,people’s awareness of data privacy has been increasing,leading to the inability to circulate data between different enterprises,resulting in the emergence of data silos.The emergence of federated learning provides a feasible solution to this problem,and the combination of federated learning and multi-robot systems can break down data silos and improve the overall performance of robots.However,as scholars have studied more deeply,they found that federated learning has very limited privacy protection.Therefore,how to protect data privacy from infringement remains an important issue.In this paper,we first give a brief introduction to the current development of multi-robot and federated learning;second,we review three aspects of privacy protection methods commonly used,privacy protection methods for multi-robot,and Other Problems Faced by Multi-robot Systems,focusing on method comparisons and challenges;and finally draw conclusions and predict possible future research directions.
