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A Privacy Enhancing Scheme for Mobile Devices Based Secure Multi-party Computation System

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Information Security and Cryptology (Inscrypt 2019)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 12020))

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Abstract

Mobile devices, such as smart phones, have recently become the typical computing platforms for many users. Consequently, in practice more and more multi-party computation systems are deployed on users’ mobile devices, resulting in various applications such as mobile outsourcing computing and mobile cooperative computing. However, as the mobile platforms may have inherent flaws, the connection of mobile devices and multi-party computation systems usually arouse new security risks. We point out that an application in one party’s mobile device can be a powerful privileged attacker to the multi-party computation system. Previous studies have mainly focused on avoiding the privacy leaks of one or several malicious parties or eavesdroppers on the Internet. This paper presents a privacy enhancing scheme for a kind of secure multi-party computation systems. The scheme can resist the privileged attackers from the party’s mobile device. Our scheme transforms the original computation process and puts the critical calculation process into trusted execution environment. We provide three components to build a privacy-enhanced multi-party computation system with our scheme. Our scheme is implemented to an actual secure multi-party computation system to demonstrate its validity and acceptable performance overhead.

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Acknowledgement

The authors would like to thank the anonymous reviewers. This work is supported by National Key R&D Program of China (No. 2017YFB0802404), and partially supported by the National Natural Science Foundation of China (No. 61802396).

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Correspondence to Na Lv .

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Yang, X., Lv, N., Chen, T., Ma, C., Liu, L. (2020). A Privacy Enhancing Scheme for Mobile Devices Based Secure Multi-party Computation System. In: Liu, Z., Yung, M. (eds) Information Security and Cryptology. Inscrypt 2019. Lecture Notes in Computer Science(), vol 12020. Springer, Cham. https://doi.org/10.1007/978-3-030-42921-8_17

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  • DOI: https://doi.org/10.1007/978-3-030-42921-8_17

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  • Online ISBN: 978-3-030-42921-8

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