Kang G. Shin
University of Michigan
915 Papers
11.3K Citations
Kang G. Shin is an academic researcher from University of Michigan. The author has contributed to research in topics: Computer science & Scheduling (computing). The author has an hindex of 98, co-authored 885 publications. Previous affiliations of Kang G. Shin include IBM & Sungkyunkwan University.
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Papers
Sequencing tasks to minimize the effects of near-coincident faults in TMR controller computers
Hagbae Kim,Kang G. Shin +1 more
TL;DR: An effective sequencing of tasks is developed to simply place an "optimal" distance (in the sense of minimizing the mean number of faulty tasks due to TMR failures) between the copies of a task to be executed on different modules.
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•Posted Content
T-TER: Defeating A2 Trojans with Targeted Tamper-Evident Routing
TL;DR: Targeted Tamper-Evident Routing (T-TER) is presented, a preventive layout-level defense against untrusted foundries, capable of thwarting the insertion of even the stealthiest hardware Trojans.
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Joint collision resolution and transmit-power adjustment for Aloha-type random access
Young-June Choi,Kang G. Shin +1 more
- 10 Feb 2013
TL;DR: A novel Cause-of-Failure resolution is proposed, where the transmit power is increased after a given number of consecutive unsuccessful access attempts when the probability that a given failure is caused by collision becomes sufficiently low.
TCP performance under aggregate fair queueing
Wei Sun,Kang G. Shin +1 more
- 29 Nov 2004
TL;DR: This paper first showed the livelock problem of FQ under the DropFront scheme, and proposed a simple solution to the problem, and then proposed a new active buffer-management scheme called ALQD+ (approximated longest queue drop), which is fairer than ALZD.
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Patent
Reducing Unregulated Aggregation Of App Usage Behaviors
Kang G. Shin,Huan Feng +1 more
- 06 Jul 2017
TL;DR: In this paper, the authors address the privacy threat of unregulated aggregation by monitoring, characterizing and reducing the underlying linkability across apps, which allows one to measure the potential threat of unsupervised aggregation during runtime and promptly warn users of the associated risks.
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