Abstract
As the most effective way to improve the efficiency of government work, e-government has been built at all levels of China, accompanied by the construction of hundreds of authentication centers, which cause serious isolation of different systems, waste of resources, inconveniences for users who have business requirements across departments and districts. Currently, users need to repeatedly register and manage multiple different accounts, or even multiple different authentication methods. In the context of population migration, cross-departmental and regional business operations are growing, it is of great significance to find trust transfer methods for different government applications.
To overcome the issue, in this paper, we first explicitly put forward a trust delivery model named “Tiger tally” that can use consensus of all the participants instead of traditional centralized structure by using blockchain. Then design a cross-domain authentication protocol that is compatible with different authentication mechanism. As our main contribution, our scheme is advanced to resolve the trust delivery issues and it is strictly considered from perspective of security, low cost and unified regulatory requirements. In particular, by integrating “HMAC”, traditionally the purview of message security with token standard, our scheme realized the idea of “Tiger tally” in ancient. It not only overcomes the long-standing trust delivery obstacles in e-government, but also achieves the traceability of responsibility and security guarantee beyond the isolated systems’ security bound.
Supported by National Key Research and Development Program of China (2017YFB0802300) and (2017YFB0802304), Science and technology projects in Sichuan Province (2017GZDZX0002) and Sichuan Science and Technology Program No.2018JY0370.
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Dong, G., Chen, Y., Hao, Y., Zhang, Z., Zhang, P., Yu, S. (2020). Tiger Tally: Cross-Domain Scheme for Different Authentication Mechanism. In: Wen, S., Zomaya, A., Yang, L. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2019. Lecture Notes in Computer Science(), vol 11944. Springer, Cham. https://doi.org/10.1007/978-3-030-38991-8_35
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