Observer-Quotient Security: Composable Leakage Bounds for Hidden State Continuations

• URL: http://arxiv.org/abs/2607.03610v1
• Date: Fri, 03 Jul 2026 21:49:34 GMT
• Title: Observer-Quotient Security: Composable Leakage Bounds for Hidden State Continuations
• Abstract summary: The paper defines observer-indexed experiments with session identifiers, adaptive schedulers, oracle forwarding, simulators, ideal quotient functionalities, and nonuniform environments.<n>The framework is instantiated for IND-CPA encryption with timing leakage, deterministic encryption with entropy ledgers, and finite-state side-channel refinement.
• Score: 0.2864713389096699
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Observer-quotient security studies interactive cryptographic systems whose security depends on what an admissible observer can distinguish across transcripts, leakage traces, and hidden implementation continuations. The paper defines observer-indexed experiments with session identifiers, adaptive schedulers, oracle forwarding, simulators, ideal quotient functionalities, and nonuniform environments, and proves a real/ideal emulation theorem in which sequential morphism defects add, parallel defects obey a product-TV bound, and adaptive observer choice is absorbed by an explicit wrapper construction. The resulting advantage bound is indexed by $δ_{\mathrm{obs},t}$, $δ_{K,t}$, $δ_{\mathrm{post},t}$, $δ_{\mathrm{sim},t}$, $η_t$, and the residual floor $ρ_T(\mathbb E Z_T)$. The framework is instantiated for IND-CPA encryption with timing leakage, deterministic encryption with entropy ledgers, and finite-state side-channel refinement under transcript, timing, cache, power, EM, and profiled observers. The optimization/control component identifies hidden continuations with observability kernels, treats sensor redesign as quotient refinement, and converts dissipativity, PL-type rates, and ISS residual bounds into concrete reductions in distinguishing advantage. Ancillary code and synthetic data reproduce the finite-state leakage audit and LTI observer-design benchmark.

Related papers

• See, Hypothesize, Validate: Multimodal Agentic Framework for Discovering Governing PDEs [1.8315988392353102]
  textbfMAGE (textbfMultimodal textbfAgentic textbfGoverning textbfEquation Discovery) is an agentic framework that organizes PDE discovery as a textitconfidence governed hypothesis validation loop<n>Discovery iterates until the top candidate clears a user-specified threshold, providing a structured process with an explicit accept-reject protocol.
  arXiv  Detail & Related papers  (2026-08-28T03:20:33Z)
• Multi-Layer Context Camouflaging: A Semantic Superposition and Contextual Lamination Framework for Malpractice-Resilient Online Assessment [0.0]
  This paper introduces the Multi-Layer Context Camouflaging Theory (MCCT), a mathematical framework that protects rendered assessment content through semantic superposition.<n> Authentic assessment content and synthetically generated camouflage are represented as a unified rendering while remaining recoverable only by legitimate candidates.
  arXiv  Detail & Related papers  (2026-08-13T11:25:12Z)
• C$^{2}$R: Cross-sample Consistency Regularization Mitigates Feature Splitting and Absorption in Sparse Autoencoders [56.67585330425431]
  Sparse Autoencoders (SAEs) are widely used to interpret large language models by decomposing activations into sparse, human-understandable features.<n>These issues stem from inconsistent latent assignment across samples.<n>We introduce C$2$R, which penalizes the co-activation of directionally similar latents.
  arXiv  Detail & Related papers  (2026-06-29T17:45:31Z)
• AnchorMoE: Interpretable Time Series Classification via Anchor-Routed MoE [51.994752158953084]
  AnchorMoE is an interpretable-by-construction classification framework.<n>It encodes multi-view representations of local patches and routes them to specialized experts.<n>Experiments on real-world and synthetic benchmarks demonstrate that AnchorMoE achieves highly competitive classification performance.
  arXiv  Detail & Related papers  (2026-06-02T13:30:54Z)
• Spectral Audit of In-Context Operator Networks [11.073238833758564]
  We introduce a Jacobian-based spectral audit for in-context operator learning.<n>Our results suggest that prediction accuracy and local operator fidelity are distinct properties of learned neural operators.
  arXiv  Detail & Related papers  (2026-06-01T16:04:21Z)
• What to Test Next: Interpretable Coverage Gap Discovery in Driving VLMs [52.50210189669399]
  Driving vision-language models (VLMs) must accurately understand scenes across diverse conditions defined by Operational Design Domains (ODDs)<n>We propose SliceScorer, a deterministic scoring rule for missing-slice recommendation.<n>We show that SliceNav surfaces high-risk coverage gaps more effectively than prior slice-discovery methods.
  arXiv  Detail & Related papers  (2026-06-01T03:18:01Z)
• Agentic Model Checking [12.832868209928039]
  We propose a paradigm that couples LLM agents with a bounded model checking backend.<n>We instantiate the approach in BMC-Agent and evaluate it on LLM-generated kernel and compiler code in C and Rust.
  arXiv  Detail & Related papers  (2026-05-20T17:25:52Z)
• Towards Causally Interpretable Wi-Fi CSI-Based Human Activity Recognition with Discrete Latent Compression and LTL Rule Extraction [10.0678616164414]
  We address Human Activity Recognition (HAR) utilizing Wi-Fi Channel State Information (CSI)<n>Deep neural models achieve strong predictive performance on CSI-based HAR (CHAR)<n>We propose a fully automatic and strictly decoupled pipeline in which CSI magnitude windows are compressed by a categorical variational autoencoder.
  arXiv  Detail & Related papers  (2026-04-24T19:45:49Z)
• IMPACT-CYCLE: A Contract-Based Multi-Agent System for Claim-Level Supervisory Correction of Long-Video Semantic Memory [73.22944697933603]
  Existing pipelines produce opaque, end-to-end outputs that expose no intermediate state for inspection.<n>We present IMPACT-Cycle, a supervisory multi-agent system that reformulates long-video understanding as a multimodal iterative claim-level maintenance.
  arXiv  Detail & Related papers  (2026-04-22T03:03:33Z)
• Beyond Reward Suppression: Reshaping Steganographic Communication Protocols in MARL via Dynamic Representational Circuit Breaking [0.0]
  steganographic collusion, where agents develop private protocols to evade monitoring, presents a critical AI safety threat.<n>Existing defenses, limited to behavioral or reward layers, fail to detect coordination in latent communication channels.<n>We introduce the Dynamic Representational Circuit Breaker (DRCB), an architectural defense operating at the optimization substrate.
  arXiv  Detail & Related papers  (2026-03-07T04:14:38Z)
• The Inevitability of Side-Channel Leakage in Encrypted Traffic [18.365496056967917]
  We show that in efficiency-prioritized systems, leakage is inevitable when at least one application pair is distinguishable.<n>This establishes the first rigorous information-theoretic foundation for encrypted traffic side channels.
  arXiv  Detail & Related papers  (2026-02-15T08:55:19Z)
• Unsupervised Conformal Inference: Bootstrapping and Alignment to Control LLM Uncertainty [49.19257648205146]
  We propose an unsupervised conformal inference framework for generation.<n>Our gates achieve close-to-nominal coverage and provide tighter, more stable thresholds than split UCP.<n>The result is a label-free, API-compatible gate for test-time filtering.
  arXiv  Detail & Related papers  (2025-09-26T23:40:47Z)
• SIM-CoT: Supervised Implicit Chain-of-Thought [108.30049193668083]
  Implicit Chain-of-Thought (CoT) methods offer a token-efficient alternative to explicit CoT reasoning in Large Language Models.<n>We identify a core latent instability issue when scaling the computational budget of implicit CoT.<n>We propose SIM-CoT, a plug-and-play training module that introduces step-level supervision to stabilize and enrich the latent reasoning space.
  arXiv  Detail & Related papers  (2025-09-24T17:01:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.