一句话：提出基于 hypertest 的多目标遗传改进方法，动态检测并降低程序中的信息泄漏，同时权衡程序语义与泄漏程度。
信息泄漏会在软件中无意暴露私有数据，但检测和修复都很困难；静态验证等方法需要专家知识且耗时。本文围绕动态、基于 hypertest 的 HyperGI 方法展开，利用遗传改进检测 hyperproperty 违规并产生潜在修复，重点针对导致信息流泄漏的 noninterference 违反。其适应度函数试图在信息泄漏与程序正确性之间取得平衡，但保持程序语义与减少泄漏之间可能存在需要开发者决策的权衡。此前实例在三个小程序上能够检测并减少泄漏，本文标题进一步强调多目标遗传改进用于泄漏检测与削减。
Information leaks in software can unintentionally reveal private data, yet they are hard to detect and fix. Although several methods have been proposed to detect leakage, such as static verification-based approaches, they require specialist knowledge, and are time-consuming. Recently, we introduced HyperGI, a dynamic, hypertest-based approach that can detect and produce potential fixes for hyperproperty violations. In particular, we focused on violations of the noninterference property, as it results in information flow leakage. Our instantiation of HyperGI was able to detect and reduce leakage in three small programs. Its fitness function tried to balance information leakage and program correctness but, as we pointed out, there may be tradeoffs between keeping program semantics and reducing information leakage that require developer decisions.
@inproceedings{paperbot7188,
title = {Keeping Secrets: Multi-objective Genetic Improvement for Detecting and Reducing Information Leakage},
author = {Ibrahim Mesecan and Daniel Blackwell and David Clark and Myra Cohen and Justyna Petke},
booktitle = {ASE},
year = {2022},
doi = {10.1145/3551349.3556947}
}