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

Metal: A Metadata-Hiding File-Sharing System

Weikeng Chen (UC Berkeley), Raluca Ada Popa (UC Berkeley)

Read More

Into the Deep Web: Understanding E-commerce Fraud from Autonomous...

Peng Wang (Indiana University Bloomington), Xiaojing Liao (Indiana University Bloomington), Yue Qin (Indiana University Bloomington), XiaoFeng Wang (Indiana University Bloomington)

Read More

Finding Safety in Numbers with Secure Allegation Escrows

Venkat Arun (Massachusetts Institute of Technology), Aniket Kate (Purdue University), Deepak Garg (Max Planck Institute for Software Systems), Peter Druschel (Max Planck Institute for Software Systems), Bobby Bhattacharjee (University of Maryland)

Read More

SODA: A Generic Online Detection Framework for Smart Contracts

Ting Chen (University of Electronic Science and Technology of China), Rong Cao (University of Electronic Science and Technology of China), Ting Li (University of Electronic Science and Technology of China), Xiapu Luo (The Hong Kong Polytechnic University), Guofei Gu (Texas A&M University), Yufei Zhang (University of Electronic Science and Technology of China), Zhou Liao (University…

Read More