Paper Info
Title
SandTrap: Tracking Information Flows On Demand with Parallel Permissions.
Abstract
The most promising way to improve the performance of dynamic information-flow tracking (DIFT) for machine code is to only track instructions when they process tainted data. Unfortunately, prior approaches to on-demand DIFT are a poor match for modern mobile platforms that rely heavily on parallelism to provide good interactivity in the face of computationally intensive tasks like image processing. The main shortcoming of these prior efforts is that they cannot support an arbitrary mix of parallel threads due to the limitations of page protections. In this paper, we identify parallel permissions as a key requirement for multithreaded, on-demand native DIFT, and we describe the design and implementation of a system called SandTrap that embodies this approach. Using our prototype implementation, we demonstrate that SandTrap's native DIFT overhead is proportional to the amount of tainted data that native code processes. For example, in the photo-sharing app Instagram, SandTrap's performance is close to baseline (1x) when the app does not access tainted data. When it does, SandTrap imposes a slowdown comparable to prior DIFT systems (~8x).
Year
DOI
Venue
2018
10.1145/3210240.3210321
MobiSys '18: The 16th Annual International Conference on Mobile Systems, Applications, and Services Munich Germany June, 2018
Keywords
Field
DocType
parallel memory permissions,dynamic information-flow tracking,native code
Interactivity,On demand,Computer science,Image processing,Thread (computing),Machine code,Embedded system
Conference
ISBN
Citations 
PageRank 
978-1-4503-5720-3
2
0.38
References 
Authors
26
6
Name
Order
Citations
PageRank
Ali Razeen1826.93
Alvin R. Lebeck261.47
David Liu3877.58
Alexander Meijer440.74
Valentin Pistol5232.13
Landon P. Cox61396109.41