Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
9-2017
Abstract
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.
Discipline
Information Security
Research Areas
Cybersecurity
Publication
Proceedings of the 22nd European Symposium on Research in Computer Security (ESORICS 2017), Oslo, Norway, September 11-15, Part II
ISBN
978-3-319-66399-9
Identifier
10.1007/978-3-319-66399-9_12
Publisher
Springer
City or Country
Germany
Citation
LIU, Weijie; GAO, Debin; and REITER, Michael K..
On-demand time blurring to support side-channel defense. (2017). Proceedings of the 22nd European Symposium on Research in Computer Security (ESORICS 2017), Oslo, Norway, September 11-15, Part II.
Available at: https://ink.library.smu.edu.sg/sis_research/4024
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
http://doi.org/10.1007/978-3-319-66399-9_12