The Queen's Guard: A Secure Enforcement of Fine-grained Access Control In Distributed Data Analytics Platforms

By Fahad Shaon, Sazzadur Rahaman, Murat Kantarcioglu. ACM Transactions on Privacy and Security, 2001

2,309 citations recorded by the source corpus.

Distributed data analytics platforms (i.e., Apache Spark, Hadoop) provide high-level APIs to programmatically write analytics tasks that are run distributedly in multiple computing nodes. The design of these frameworks was primarily motivated by performance and usability. Thus, the security takes a back seat. Consequently, they do not inherently support fine-grained access control or offer any plugin mechanism to enable it, making them risky to be used in multi-tier organizational settings. There have been attempts to build “add-on” solutions to enable fine-grained access control for distributed data analytics platforms. In this paper, first, we show that straightforward enforcement of “add-on” access control is insecure under adversarial code execution. Specifically, we show that an attacker can abuse platform-provided APIs to evade access controls without leaving any traces. Second, we designed a two-layered (i.e., proactive and reactive) defense system to protect against API abuses. On submission of a user code, our proactive security layer statically screens it to find potential attack signatures prior to its execution. The reactive security layer employs code instrumentation-based runtime checks and sandboxed execution to throttle any exploits at runtime. Next, we propose a new fine-grained access control framework with an enhanced policy language that supports map and filter primitives. Finally, we build a system named SecureDL with our new access control framework and defense system on top of Apache Spark, which ensures secure access control policy enforcement under adversaries capable of executing code. To the best of our knowledge, this is the first fine-grained attribute-based access control framework for distributed data analytics platforms that is secure against platform API abuse attacks. Performance evaluation showed that the overhead due to added security is low.

Subfields: Artificial Intelligence, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/1a31a3d141e15f4d41f8fe1b262e89903c9bd9c6

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Citations in the corpus

Reinforcement learning based resource allocation in business process management (2011, background): “Authorization considerations Early works focused on authorization considerations of resource allocation, and the corresponding mechanisms have been provided, including the Role-Based Access Control model [49], which tends to focus on security considerations and neglects other organizational aspects,”
Model driven security: From UML models to access control infrastructures (2006, methodology): “First, we specify a security modeling language for modeling access control requirements that generalizes role-based access control (RBAC) [10].”
SecureUML: A UML-Based Modeling Language for Model-Driven Security (2002, methodology): “Such constraints are also defined in [4].”
Securing Personal Health Records in Cloud Computing: Patient-Centric and Fine-Grained Data Access Control in Multi-owner Settings (2010, background): “Various access control models have been proposed and applied, including role-based (RBAC) and attribute-based access control (ABAC) [10].”
Database security - concepts, approaches, and challenges (2005, background): “Restrictions apply.”
Security and privacy in electronic health records: A systematic literature review (2013, methodology): “RBAC has been implemented in many commercial systems [45], and an RBAC-standard has therefore been created to ensure that the main principles remain equal across different implementations [46,119].”
Towards comprehensive support for organizational mining (2008, methodology): “RBAC (Role based access control) [46, 24] is one of the more popular techniques to manage resources in workflow area [11, 12, 29, 49].”
Fog computing security: a review of current applications and security solutions (2017, methodology): “In this case, identity-based encryption algorithms [85] and RoleBased Access Control model, as suggested by NIST [86], can be implemented to increase security.”
Business Process Management: A Comprehensive Survey (2013, methodology): “Role-Based Access Control (RBAC, [115]) techniques can be applied in this setting.”
A Unified Attribute-Based Access Control Model Covering DAC, MAC and RBAC (2012, background): “,subject and object label do not change)and RBAC to mean core or flat RBAC (RBAC0), and hierarchical RBAC (RBAC1) [11,23].”
Organization based access control (2003, methodology): “Several access control models have been proposed: DAC[1], MAC[2, 3], RBAC[4, 5, 6], TBAC[7] or TMAC[8].”
Privacy-aware role based access control (2007, methodology): “Among those models, RBAC [Ferraiolo et al. 2001] and the Usage Control (UCON) models [Park and Sandhu 2004] are the closest to P-RBAC.”