LeakProber: a framework for profiling sensitive data leakage paths

  • Authors:

  • Junfeng Yu;Shengzhi Zhang;Peng Liu;ZhiTang Li

  • Affiliations:

  • Huazhong University of Science and Technology, Wuhan, China and Pennsylvania State University, State College, PA, USA;Pennsylvania State University, State College, PA, USA;Pennsylvania State University, State College, PA, USA;Huazhong University of Science and Technology, Wuhan, China

  • Venue:
  • Proceedings of the first ACM conference on Data and application security and privacy
  • Year:
  • 2011

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Abstract

In this paper, we present the design, implementation, and evaluation of LeakProber, a framework that leverages the whole system dynamic instrumentation and the inter-procedural analysis to enable data propagation path profiling in production system. We integrate both the static analysis and runtime tracking to establish a holistic and practical approach to generating the sensitive data propagation graph (sDPG) with minimum runtime overhead. We evaluate our system on several data stealing attacks scenario for generating sDPG. The sDPG generated by our system captures multiple aspects of data accessing patterns and provides clear insights into the data leakage path. We also measure the performance of our system and find that it degrades the production system about 6% in the trace-on mode. When our prototype works in the trace-off mode, the runtime overhead is even lower, on an average of 1.5% across each benchmark we run. We believe that it is feasible to directly apply our prototype into production system environment.