Abstract
The ongoing threat of malware has raised significant security and privacy concerns. Motivated by these issues, the cloud-based detection system is of increasing interest to detect large-scale malware as it releases the burden of client and improves the detection efficiency. However, most existing cloud-based detection systems overlook the data privacy protection during the malware detection. In this paper, we propose a cloud-based anti-malware system named PriMal, which protects the data privacy of both the cloud server and the client, while still achieves usable detection performance. In the PriMal, a newly designed private malware signature set intersection (PMSSI) protocol is involved to enable both the cloud server and client to achieve malware confirmation without revealing the data privacy in semi-honest model. Moreover, we propose the relevant signature engine to reduce the detection range and overhead. The experimental results show that PriMal offers a practical approach to achieve both usable malware detection and strong data privacy preservation.
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Notes
- 1.
In the field of secure computation, the semi-honest model is not the strongest model but it is widely accepted and used in many applications. Hence, we conclude the protection is strong as compared to Level II.
- 2.
The cloud server has to ask for the permission of client if the detection results are needed to improve the security service.
- 3.
Modulo(q) hash function [9] randomly maps a byte to a class between 0 to \(q-1\), q is the power of 2 and smaller than 256.
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Acknowledgement
This research is supported in part by the project of Guangxi cooperative innovation center of cloud computing and big data No. YD16505. The authors gratefully thank the anonymous reviewers for their helpful comments.
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Sun, H., Su, J., Wang, X., Chen, R., Liu, Y., Hu, Q. (2017). PriMal: Cloud-Based Privacy-Preserving Malware Detection. In: Pieprzyk, J., Suriadi, S. (eds) Information Security and Privacy. ACISP 2017. Lecture Notes in Computer Science(), vol 10343. Springer, Cham. https://doi.org/10.1007/978-3-319-59870-3_9
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