内容保护的第一步剽窃检测
A First Step Towards Content Protecting Plagiarism Detection
摘要：
剽窃检测系统是维护学术和教育诚信的重要工具。然而，当今的系统需要公开输入文档的全部内容以及输入文档与之进行比较的文档集合。此外，这些系统是集中式的，由个别的、通常是商业供应商控制。这种情况引起了关于敏感数据保密性的程序和法律问题，这可能会限制或禁止使用剽窃检测服务。为了消除当前系统的这些弱点，我们寻求设计一种剽窃检测方法，该方法不需要集中式提供商，也不需要将任何内容暴露为明文。本文介绍了我们的研究的初步结果。具体来说，我们采用私有集交叉设计一个内容保护的变种，在我们的剽窃检测系统HyPlag中实现的基于引用的相似性度量书目耦合。我们的评估表明，内容保护方法达到了相同的检测效果，而原来的方法，使常见的攻击，以揭示受保护的内容实际上是不可行的。我们未来的工作将通过设计剽窃检测方法来扩展这种成功的概念验证，这种方法可以分析文档的整个内容，而不会将其作为明文披露。
英文摘要：
Plagiarism detection systems are essential tools for safeguarding academic and educational integrity. However, today's systems require disclosing the full content of the input documents and the document collection to which the input documents are compared. Moreover, the systems are centralized and under the control of individual, typically commercial providers. This situation raises procedural and legal concerns regarding the confidentiality of sensitive data, which can limit or prohibit the use of plagiarism detection services. To eliminate these weaknesses of current systems, we seek to devise a plagiarism detection approach that does not require a centralized provider nor exposing any content as cleartext. This paper presents the initial results of our research. Specifically, we employ Private Set Intersection to devise a content-protecting variant of the citation-based similarity measure Bibliographic Coupling implemented in our plagiarism detection system HyPlag. Our evaluation shows that the content-protecting method achieves the same detection effectiveness as the original method while making common attacks to disclose the protected content practically infeasible. Our future work will extend this successful proof-of-concept by devising plagiarism detection methods that can analyze the entire content of documents without disclosing it as cleartext.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
