Comparison of Access Control Approaches for Graph-Structured Data

TL;DR

This paper systematically reviews and compares various access control methods for graph-structured data, focusing on their approaches, policies, enforcement, and limitations to guide future research.

Contribution

It provides a detailed comparison of recent access control approaches for property graph data, highlighting their strengths and weaknesses.

Findings

Different approaches vary in base models and policy types.

Most methods support fine-grained access control.

Limitations include lack of datastore independence and negative permission support.

Abstract

Access control is the enforcement of the authorization policy, which defines subjects, resources, and access rights. Graph-structured data requires advanced, flexible, and fine-grained access control due to its complex structure as sequences of alternating vertices and edges. Several research works focus on protecting property graph-structured data, enforcing fine-grained access control, and proving the feasibility and applicability of their concept. However, they differ conceptually and technically. We select works from our systematic literature review on authorization and access control for different database models in addition to recent ones. Based on defined criteria, we exclude research works with different objectives, such as no protection of graph-structured data, graph models other than the property graph, coarse-grained access control approaches, or no application in a graph datastore (i.e., no proof-of-concept implementation). The latest version of the remaining works are discussed in detail in terms of their access control approach as well as authorization policy definition and enforcement. Finally, we analyze the strengths and limitations of the selected works and provide a comparison with respect to different aspects, including the base access control model, open/closed policy, negative permission support, and datastore-independent enforcement.