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On-demand time blurring to support side-channel defense

Publication ,  Conference
Liu, W; Gao, D; Reiter, MK
Published in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
January 1, 2017

Side-channel attacks are a serious threat to multi-tenant public clouds. Past work showed how secret information in one virtual machine (VM) can be leaked to another, co-resident VM using timing side channels. Recent defenses against timing side channels focus on reducing the degree of resource sharing. However, such defenses necessarily limit the flexibility with which resources are shared. In this paper, we propose a technique that dynamically adjusts the granularity of platform time sources, to interfere with timing side-channel attacks. Our proposed technique supposes an interface by which a VM can request the temporary coarsening of platform time sources as seen by all VMs on the platform, which the hypervisor can effect since it virtualizes accesses to those timers. We show that the VM-Function (VMFUNC) mechanism provides a low-overhead such interface, thereby enabling applications to adjust timer granularity with minimal overhead. We present a proof-of-concept implementation using a Xen hypervisor running Linux-based VMs on a cloud server using commodity Intel processors and supporting adjustment of the timestamp-counter (TSC) granularity. We evaluate our implementation and show that our scheme mitigates timing side-channel attacks, while introducing negligible performance penalties.

Duke Scholars

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

EISSN

1611-3349

ISSN

0302-9743

Publication Date

January 1, 2017

Volume

10493 LNCS

Start / End Page

210 / 228

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences
 

Citation

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Liu, W., Gao, D., & Reiter, M. K. (2017). On-demand time blurring to support side-channel defense. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10493 LNCS, pp. 210–228). https://doi.org/10.1007/978-3-319-66399-9_12
Liu, W., D. Gao, and M. K. Reiter. “On-demand time blurring to support side-channel defense.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10493 LNCS:210–28, 2017. https://doi.org/10.1007/978-3-319-66399-9_12.
Liu W, Gao D, Reiter MK. On-demand time blurring to support side-channel defense. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2017. p. 210–28.
Liu, W., et al. “On-demand time blurring to support side-channel defense.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10493 LNCS, 2017, pp. 210–28. Scopus, doi:10.1007/978-3-319-66399-9_12.
Liu W, Gao D, Reiter MK. On-demand time blurring to support side-channel defense. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2017. p. 210–228.

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

EISSN

1611-3349

ISSN

0302-9743

Publication Date

January 1, 2017

Volume

10493 LNCS

Start / End Page

210 / 228

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences