Ren Ding (Georgia Institute of Technology), Hong Hu (Georgia Institute of Technology), Wen Xu (Georgia Institute of Technology), Taesoo Kim (Georgia Institute of Technology)

Software vendors collect crash reports from end-users to assist debugging and testing of their products. However, crash reports may contain user’s private information, like names and passwords, rendering users hesitated to share the crash report with developers. We need a mechanism to protect user’s privacy from crash reports on the client-side, and meanwhile, keep sufficient information to support server-side debugging.

In this paper, we propose the DESENSITIZATION technique that generates privacy-aware and attack-preserving crash reports from crashed processes. Our tool uses lightweight methods to identify bug- and attack-related data from the memory, and removes other data to protect user’s privacy. Since the desensitized memory has more null bytes, we store crash reports in spare files to save the network bandwidth and the server-side storage. We prototype DESENSITIZATION and apply it to a large number of crashes from several real-world programs, like browser and JavaScript engine. The result shows that our DESENSITIZATION technique can eliminate 80.9% of non-zero bytes from coredumps, and 49.0% from minidumps. The desensitized crash report can be 50.5% smaller than the original size, which significantly saves resources for report submission and storage. Our DESENSITIZATION technique is a push-button solution for the privacy-aware crash report.

View More Papers

Proof of Storage-Time: Efficiently Checking Continuous Data Availability

Giuseppe Ateniese (Stevens Institute of Technology), Long Chen (New Jersey Institute of Technology), Mohammard Etemad (Stevens Institute of Technology), Qiang Tang (New Jersey Institute of Technology)

Read More

FUSE: Finding File Upload Bugs via Penetration Testing

Taekjin Lee (KAIST, ETRI), Seongil Wi (KAIST), Suyoung Lee (KAIST), Sooel Son (KAIST)

Read More

DefRec: Establishing Physical Function Virtualization to Disrupt Reconnaissance of...

Hui Lin (University of Nevada, Reno), Jianing Zhuang (University of Nevada, Reno), Yih-Chun Hu (University of Illinois, Urbana-Champaign), Huayu Zhou (University of Nevada, Reno)

Read More

Precisely Characterizing Security Impact in a Flood of Patches...

Qiushi Wu (University of Minnesota), Yang He (University of Minnesota), Stephen McCamant (University of Minnesota), Kangjie Lu (University of Minnesota)

Read More