SecureDL: Securing Code Execution and Access Control for Distributed Data Analytics Platforms

06/24/2021
by   Fahad Shaon, et al.
0

Distributed data analytics platforms such as Apache Spark enable cost-effective processing and storage. These platforms allow users to distribute data to multiple nodes and enable arbitrary code execution over this distributed data. However, such capabilities create new security and privacy challenges. First, the user-submitted code may potentially contain malicious code to circumvent existing security checks. In addition, providing fine-grained access control for different types of data (e.g., text, images, etc.) may not be feasible for different data storage options. To address these challenges, we provide a fine-grained access control framework tailored for distributed data analytics platforms, which is protected against evasion attacks with two distinct layers of defense. Access control is implemented with runtime injection of access control logic on a submitted data analysis job. The proactive security layer utilizes state-of-the-art program analysis to detect potentially malicious user code. The reactive security layer consists of binary integrity checking, instrumentation-based runtime checks, and sandboxed execution. To the best of our knowledge, this is the first work that provides fine-grained attribute-based access control for distributed data analytics platforms using code rewriting and static program analysis. Furthermore, we evaluated the performance of our security system under different settings and show that the performance overhead due to added security is low.

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