ArXiv TLDR

Compositional security definitions for higher-order where declassification

🐦 Tweet
2604.18300

Jan Menz, Andrew K. Hirsch, Peixuan Li, Deepak Garg

cs.PLcs.CR

TLDR

This paper introduces a compositional security definition for 'where declassification' in higher-order programs using logical relations, enhancing data privacy.

Key contributions

  • Introduces a compositional security definition for 'where declassification' in higher-order programs.
  • Develops a new security model using logical relations for higher-order declassification.
  • Key insight: stop enforcing indistinguishability after a relevant declassification event.
  • Provides stronger security guarantees compared to existing lower-order declassification definitions.

Why it matters

This paper addresses a critical gap by providing the first compositional security definition for 'where declassification' in higher-order programs. Its new model offers stronger formal guarantees for data privacy, significantly advancing information-flow security in complex systems.

Original Abstract

To ensure programs do not leak private data, we often want to be able to provide formal guarantees ensuring such data is handled correctly. Often, we cannot keep such data secret entirely; instead programmers specify how private data may be declassified. While security definitions for declassification exist, they mostly do not handle higher-order programs. In fact, in the higher-order setting no compositional security definition exists for intensional information-flow properties such as where declassification, which allows declassification in specific parts of a program. We use logical relations to build a model (and thus security definition) of where declassification. The key insight required for our model is that we must stop enforcing indistinguishability once a \emph{relevant declassification} has occurred. We show that the resulting security definition provides more security than the most related previous definition, which is for the lower-order setting. This paper is an extended version of the paper of the same name published at OOPSLA 2023 ([21]).

📬 Weekly AI Paper Digest

Get the top 10 AI/ML arXiv papers from the week — summarized, scored, and delivered to your inbox every Monday.