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

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Laputa is a security framework for Apache Spark that enforces fine-grained data access policies and protects against malicious users and cloud managers. It uses pattern matching to check policies at the physical plan level and leverages confidential computing to compartmentalize Spark applications, ensuring the integrity of the entire data analysis pipeline. Evaluation on TPC-H benchmarks and real-world ML applications shows Laputa successfully blocks malicious applications with moderate performance overhead while requiring minimal modifications to existing Spark code.

NDSS 2025 – Secure Data Analytics