一句话：为 Spark 类大数据平台提出单元格级污点追踪框架，通过动态代码翻译注入污点逻辑，自动执行隐私策略。
大数据平台（如 Spark）中，开发者提交脚本处理原始与中间数据表，但难以灵活、自动地满足各类隐私合规要求。本文提出 TaintStream，一个面向类 Spark 大数据平台的细粒度污点追踪框架。其核心思路是自动向数据处理脚本注入污点追踪逻辑，并在执行时对脚本进行动态翻译，为每个数据单元格维护污点标签；动态翻译规则经过精心设计，保证不干扰（non-interference）原有数据操作语义。通过定义不同的污点标签语义，TaintStream 可支撑访问控制、数据保留与用户数据擦除等多种数据管理应用。在自建基准测试上，TaintStream 实现了单元格级精确污点追踪，精度达 93.0%，额外开销低于 15%；作者还通过若干真实隐私策略执行用例展示了其实用性。
Big data has become valuable property for enterprises and enabled various intelligent applications. Today, it is common to host data in big data platforms (e.g., Spark), where developers can submit scripts to process the original and intermediate data tables. Meanwhile, it is highly desirable to manage the data to comply with various privacy requirements. To enable flexible and automated privacy policy enforcement, we propose TaintStream, a fine-grained taint tracking framework for Spark-like big data platforms. TaintStream works by automatically injecting taint tracking logic into the data processing scripts, and the injected scripts are dynamically translated to maintain a taint tag for each cell during execution. The dynamic translation rules are carefully designed to guarantee non-interference in the original data operation. By defining different semantics of taint tags, TaintStream can enable various data management applications such as access control, data retention, and user data erasure. Our experiments on a self-crafted benchmarksuite show that TaintStream is able to achieve accurate cell-level taint tracking with a precision of 93.0% and less than 15% overhead. We also demonstrate the usefulness of TaintStream through several real-world use cases of privacy policy enforcement.
DOI 原文 ·
@inproceedings{paperbot6288,
title = {TaintStream: fine-grained taint tracking for big data platforms through dynamic code translation},
author = {Chengxu Yang and Yuanchun Li and Mengwei Xu and Zhenpeng Chen and Yunxin Liu and Gang Huang and Xuanzhe Liu},
booktitle = {FSE},
year = {2021},
doi = {10.1145/3468264.3468532}
}