Robustly Safe Compilation or, Efficient, Provably Secure Compilation
April 02, 2018 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Marco Patrignani, Deepak Garg
arXiv ID
1804.00489
Category
cs.PL: Programming Languages
Citations
10
Venue
arXiv.org
Last Checked
3 months ago
Abstract
Secure compilers generate compiled code that withstands many target-level attacks such as alteration of control flow, data leaks or memory corruption. Many existing secure compilers are proven to be fully abstract, meaning that they reflect and preserve observational equivalence. Fully abstract compilation is a strong and useful property that, in certain cases, comes at the cost of requiring expensive runtime constructs in compiled code. These constructs may have no relevance for security, but are needed to accommodate differences between the source language and the target language that fully abstract compilation necessarily regards. As an alternative to fully abstract compilation, this paper explores a different criterion for secure compilation called robustly safe compilation or RSC. Briefly, this criterion means that the compiled code preserves relevant safety properties of the source program against all adversarial contexts interacting with said program. We show that RSC can be attained easily and results in code that is more efficient than that generated by fully abstract compilers. We also develop three illustrative robustly-safe compilers and, through them, develop two different proof techniques for establishing that a compiler attains RSC. Through these, we also establish that proving RSC is simpler than proving fully abstraction.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Programming Languages
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions
R.I.P.
π»
Ghosted
Glow: Graph Lowering Compiler Techniques for Neural Networks
R.I.P.
π»
Ghosted
Learnable Programming: Blocks and Beyond
R.I.P.
π»
Ghosted
Scenic: A Language for Scenario Specification and Scene Generation
R.I.P.
π»
Ghosted
Vandal: A Scalable Security Analysis Framework for Smart Contracts
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted