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ICSE 2021
Sun 16 May - Sat 5 June 2021

This program is tentative and subject to change.

We propose a data-driven method for synthesizing a static analyzer to detect side-channel information leaks in cryptographic software. Compared to the conventional way of manually crafting such a static analyzer, which can be labor intensive, error prone and suboptimal, our learning-based technique is not only automated but also provably sound. Our analyzer consists of a set of type-inference rules learned from the training data, i.e., example code snippets annotated with ground truth. Internally, we use syntax-guided synthesis (SyGuS) to generate new features and decision tree learning (DTL) to generate type-inference rules based on these features. We guarantee soundness by formally proving each learned rule via a technique called Datalog query containment checking. We have implemented our technique in the LLVM compiler and used it to detect power side channels in C programs. Our results show that, in addition to being automated and provably sound during synthesis, the learned analyzer also has the same empirical accuracy as two state-of-the-art, manually crafted analyzers while being 300X and 900X faster, respectively.

This program is tentative and subject to change.

Tue 25 May
Times are displayed in time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change

16:40 - 17:35
1.4.3. Identifying Information LeaksTechnical Track / NIER - New Ideas and Emerging Results at Blended Sessions Room 3
Chair(s): Oscar DiesteUniversidad Polit├ęcnica de Madrid
16:40
15m
Paper
An Axiomatic Approach to Detect Information Leaks in Concurrent ProgramsNIER
NIER - New Ideas and Emerging Results
Sandip GhosalIndian Institute of Technology, Bombay, R.K. ShyamasundarIndian Institute of Technology, Bombay
Pre-print
16:55
20m
Paper
Abacus: Precise Side-Channel AnalysisTechnical TrackArtifact Available
Technical Track
Qinkun BaoThe Pennsylvania State University, Zihao WangThe Pennsylvania State University, Xiaoting LiPenn State University, James LarusEPFL, Dinghao WuThe Pennsylvania State University
Pre-print Media Attached
17:15
20m
Paper
Data-Driven Synthesis of a Provably Sound Side Channel AnalysisTechnical Track
Technical Track
Jingbo WangUniversity of Southern California, Chungha SungUniversity of Southern California, Mukund RaghothamanUniversity of Southern California, Chao WangUSC
Pre-print