NDSS 2025 – Secure Data Analytics
嗯,用户让我帮忙总结一篇文章的内容,控制在一百个字以内,而且不需要特定的开头。首先,我需要仔细阅读文章内容,找出关键信息。 文章主要讲的是Laputa框架,用于在Apache Spark中进行安全数据分析。作者提到现有的Spark平台虽然方便数据共享和分析,但缺乏有效的策略执行机制,导致数据泄露的风险。Laputa通过细粒度的策略检查和隔离执行来解决这个问题。 接下来,我需要提取核心要点:Laputa的作用、它如何工作(物理计划检查和隔离)、以及它的优势(保护数据管道、性能影响小)。然后把这些信息浓缩成简洁的一段话。 最后,确保字数不超过一百字,并且表达清晰。这样用户就能快速了解文章的主要内容了。 </think> 该研究提出了一种名为Laputa的安全策略执行框架,用于在Apache Spark中实现细粒度的数据分析策略控制和隔离执行。通过物理计划级别的模式匹配策略检查和基于 confidential computing 的应用隔离,Laputa 保护了数据管道的完整性,同时保持了良好的性能表现。 2026-1-24 16:0:0 Author: securityboulevard.com(查看原文) 阅读量:0 收藏

Session 10A: Confidential Computing 2

Authors, Creators & Presenters: Byeongwook Kim (Seoul National University), Jaewon Hur (Seoul National University), Adil Ahmad (Arizona State University), Byoungyoung Lee (Seoul National University)
PAPER
Secure Data Analytics in Apache Spark with Fine-grained Policy Enforcement and Isolated Execution
Cloud based Spark platform is a tempting approach for sharing data, as it allows data users to easily analyze the data while the owners to efficiently share the large volume of data. However, the absence of a robust policy enforcement mechanism on Spark hinders the data owners from sharing their data due to the risk of private data breach. In this respect, we found that malicious data users and cloud managers can easily leak the data by constructing a policy violating physical plan, compromising the Spark libraries, or even compromising the Spark cluster itself. Nonetheless, current approaches fail to securely and generally enforce the policies on Spark, as they do not check the policies on physical plan level, and they do not protect the integrity of data analysis pipeline. This paper presents Laputa, a secure policy enforcement framework on Spark. Specifically, Laputa designs a pattern matching based policy checking on the physical plans, which is generally applicable to Spark applications with more fine-grained policies. Then, Laputa compartmentalizes Spark applications based on confidential computing, by which the entire data analysis pipeline is protected from the malicious data users and cloud managers. Meanwhile, Laputa preserves the usability as the data users can run their Spark applications on Laputa with minimal modification. We implemented Laputa, and evaluated its security and performance aspects on TPC-H, Big Data benchmarks, and real world applications using ML models. The evaluation results demonstrated that Laputa correctly blocks malicious Spark applications while imposing moderate performance overheads.
ABOUT NDSS
The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.

Our thanks to the Network and Distributed System Security (NDSS) Symposium for publishing their Creators, Authors and Presenter’s superb NDSS Symposium 2025 Conference content on the Organizations’ YouTube Channel.

Permalink

*** This is a Security Bloggers Network syndicated blog from Infosecurity.US authored by Marc Handelman. Read the original post at: https://www.youtube-nocookie.com/embed/vHcpDk2WsnM?si=-WNkxii2qvHy0k37


文章来源: https://securityboulevard.com/2026/01/ndss-2025-secure-data-analytics/
如有侵权请联系:admin#unsafe.sh