Function-Based Access Control (FBAC): From Access Control Matrix to Access Control Tensor
September 15, 2016 ยท Declared Dead ยท ๐ MIST@CCS
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Yvo Desmedt, Arash Shaghaghi
arXiv ID
1609.04514
Category
cs.CR: Cryptography & Security
Citations
7
Venue
MIST@CCS
Last Checked
3 months ago
Abstract
Security researchers have stated that the core concept behind current implementations of access control predates the Internet. These assertions are made to pinpoint that there is a foundational gap in this field, and one should consider revisiting the concepts from the ground up. Moreover, Insider threats, which are an increasing threat vector against organizations are also associated with the failure of access control. Access control models derived from access control matrix encompass three sets of entities, Subjects, Objects and Operations. Typically, objects are considered to be files and operations are regarded as Read, Write, and Execute. This implies an `open sesame' approach when granting access to data, i.e. once access is granted, there is no restriction on command executions. Inspired by Functional Encryption, we propose applying access authorizations at a much finer granularity, but instead of an ad-hoc or computationally hard cryptographic approach, we postulate a foundational transformation to access control. From an abstract viewpoint, we suggest storing access authorizations as a three-dimensional tensor, which we call Access Control Tensor (ACT). In Function-based Access Control (FBAC), applications do not give blind folded execution right and can only invoke commands that have been authorized for data segments. In other words, one might be authorized to use a certain command on one object, while being forbidden to use exactly the same command on another object. The theoretical foundations of FBAC are presented along with Policy, Enforcement and Implementation (PEI) requirements of it. A critical analysis of the advantages of deploying FBAC, how it will result in developing a new generation of applications, and compatibility with existing models and systems is also included. Finally, a proof of concept implementation of FBAC is presented.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
๐ Similar Papers
In the same crypt โ Cryptography & Security
R.I.P.
๐ป
Ghosted
R.I.P.
๐ป
Ghosted
Membership Inference Attacks against Machine Learning Models
R.I.P.
๐ป
Ghosted
The Limitations of Deep Learning in Adversarial Settings
R.I.P.
๐ป
Ghosted
Practical Black-Box Attacks against Machine Learning
R.I.P.
๐ป
Ghosted
Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks
R.I.P.
๐ป
Ghosted
Extracting Training Data from Large Language Models
Died the same way โ ๐ป Ghosted
R.I.P.
๐ป
Ghosted
Language Models are Few-Shot Learners
R.I.P.
๐ป
Ghosted
PyTorch: An Imperative Style, High-Performance Deep Learning Library
R.I.P.
๐ป
Ghosted
XGBoost: A Scalable Tree Boosting System
R.I.P.
๐ป
Ghosted