ABSTRACT Data integrity is a key component of effective Bayesian network structure learning algorithms, namely PC algorithm, design and use. Given the role that integrity of data plays in these outcomes, this research demonstrates the importance of data integrity as a key component in machine learning tools in order to emphasize the need for carefully considering data integrity during tool development and utilization. To meet this purpose, we study how an adversary could generate a desired network with the PC algorithm. Given a Bayesian network and a database generated by and a second Bayesian network, , which is equal to , except for a minor change like a missing link, a reversed link, or an additional link, we explore and analyze what is the minimal number of changes such as additions, deletions, substitutions to that lead to a database that, when given as input to PC algorithm, results in .

中文翻译：

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针对PC学习算法的对抗性数据中毒攻击

摘要 数据完整性是有效的贝叶斯网络结构学习算法，即 PC 算法、设计和使用的关键组成部分。鉴于数据完整性在这些结果中所起的作用，本研究证明了数据完整性作为机器学习工具的关键组成部分的重要性，以强调在工具开发和使用过程中仔细考虑数据完整性的必要性。为了达到这个目的，我们研究了对手如何使用 PC 算法生成所需的网络。给定一个贝叶斯网络和一个由 和第二个贝叶斯网络生成的数据库，它等于 ，除了像缺失链接、反向链接或附加链接这样的微小变化，我们探索和分析最小数量是多少导致数据库的添加、删除、替换等更改，