On Nov.4th, AQSIQ (General Administration of Quality Supervision, Inspection and Quarantine of the People’s Republic of China), SAC (Standardization Administration of China), National Audit Office of China (CNAO), an...On Nov.4th, AQSIQ (General Administration of Quality Supervision, Inspection and Quarantine of the People’s Republic of China), SAC (Standardization Administration of China), National Audit Office of China (CNAO), and National Ministry of Finance of China jointly held the conference press on the national standard of Information Technology-Data Interface of Accounting Software (GB/T 19581-2004) in Beijing. The standard was approved and issued on Sept. 20, 2004 by AQSIQ and SAC, and it would come into effect all over the whole nation from January 1st, 2005.展开更多
In Decentralized Machine Learning(DML)systems,system participants contribute their resources to assist others in developing machine learning solutions.Identifying malicious contributions in DML systems is challenging,...In Decentralized Machine Learning(DML)systems,system participants contribute their resources to assist others in developing machine learning solutions.Identifying malicious contributions in DML systems is challenging,which has led to the exploration of blockchain technology.Blockchain leverages its transparency and immutability to record the provenance and reliability of training data.However,storing massive datasets or implementing model evaluation processes on smart contracts incurs high computational costs.Additionally,current research on preventing malicious contributions in DML systems primarily focuses on protecting models from being exploited by workers who contribute incorrect or misleading data.However,less attention has been paid to the scenario where malicious requesters intentionally manipulate test data during evaluation to gain an unfair advantage.This paper proposes a transparent and accountable training data sharing method that securely shares data among potentially malicious system participants.First,we introduce a blockchain-based DML system architecture that supports secure training data sharing through the IPFS network.Second,we design a blockchain smart contract to transparently split training datasets into training and test datasets,respectively,without involving system participants.Under the system,transparent and accountable training data sharing can be achieved with attribute-based proxy re-encryption.We demonstrate the security analysis for the system,and conduct experiments on the Ethereum and IPFS platforms to show the feasibility and practicality of the system.展开更多
文摘On Nov.4th, AQSIQ (General Administration of Quality Supervision, Inspection and Quarantine of the People’s Republic of China), SAC (Standardization Administration of China), National Audit Office of China (CNAO), and National Ministry of Finance of China jointly held the conference press on the national standard of Information Technology-Data Interface of Accounting Software (GB/T 19581-2004) in Beijing. The standard was approved and issued on Sept. 20, 2004 by AQSIQ and SAC, and it would come into effect all over the whole nation from January 1st, 2005.
基金supported by the MSIT(Ministry of Science and ICT),Korea,under the Special R&D Zone Development Project(R&D)—Development of R&D Innovation Valley support program(2023-DD-RD-0152)supervised by the Innovation Foundation.It was also partially supported by the Ministry of Science and ICT(MSIT),Korea,under the Information Technology Research Center(ITRC)support program(IITP-2024-2020-0-01797)supervised by the Institute for Information&Communications Technology Planning&Evaluation(IITP).
文摘In Decentralized Machine Learning(DML)systems,system participants contribute their resources to assist others in developing machine learning solutions.Identifying malicious contributions in DML systems is challenging,which has led to the exploration of blockchain technology.Blockchain leverages its transparency and immutability to record the provenance and reliability of training data.However,storing massive datasets or implementing model evaluation processes on smart contracts incurs high computational costs.Additionally,current research on preventing malicious contributions in DML systems primarily focuses on protecting models from being exploited by workers who contribute incorrect or misleading data.However,less attention has been paid to the scenario where malicious requesters intentionally manipulate test data during evaluation to gain an unfair advantage.This paper proposes a transparent and accountable training data sharing method that securely shares data among potentially malicious system participants.First,we introduce a blockchain-based DML system architecture that supports secure training data sharing through the IPFS network.Second,we design a blockchain smart contract to transparently split training datasets into training and test datasets,respectively,without involving system participants.Under the system,transparent and accountable training data sharing can be achieved with attribute-based proxy re-encryption.We demonstrate the security analysis for the system,and conduct experiments on the Ethereum and IPFS platforms to show the feasibility and practicality of the system.
