To Overcome the Obstacle of Data Exchange to Increase the Utilization Efficiency of Accounting Data

On Nov.4^(th), AQSIQ (General Administration of Quality Supervision,Inspection and Quarantine of the People' s Republic of China), SAC (Standardization Administrationof China), National Audit Office of China (CNAO), and National Ministry of Finance of China jointlyheld the conference press on the national standard of Information Technology--Data Interface ofAccounting 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 1^(st),2005. Pu Changcheng, Vice Director of AQSIQ, Shi Aizhong, Vice Director of CNAO, Li Zhonghai. amember of the Party Group of AQSIQ and Director of SAC, the other leaders of concerned departmentssuch as National Ministry of Finance, National Telegraphy Office, and etc. attended the ConferencePress and made speeches. They fully affirmed the important significance and the achievements onstandardization work of electronic government business, and also they set new demands on the workfor the future.

Transparent and Accountable Training Data Sharing in Decentralized Machine Learning Systems

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.