timing-oracle-core

Core statistical analysis for timing side-channel detection.

This crate provides the fundamental statistical algorithms used by timing-oracle, designed to work in no_std environments (embedded, WASM, SGX enclaves) with only an allocator.

When to Use This Crate

Most users should use the main timing-oracle crate, which provides:

• Measurement collection and timing infrastructure
• Adaptive sampling orchestration
• Pretty terminal output and JSON formatting
• The timing_test! macro

Use timing-oracle-core directly only if you need:

• no_std compatibility (embedded, WASM, SGX)
• Custom measurement collection
• Direct access to statistical primitives

Features

• std (default): Standard library support
• parallel: Parallel bootstrap using rayon (requires std, 4-8x speedup)

For no_std, disable default features:

[dependencies]
timing-oracle-core = { version = "0.1", default-features = false }

What's Included

Statistical Analysis

• Quantile-based test statistics (9 deciles)
• Block bootstrap for covariance estimation
• Bayesian posterior probability computation

Result Types

• Outcome - Pass/Fail/Inconclusive/Unmeasurable
• EffectEstimate - Decomposed timing effect (shift + tail)
• Exploitability - Negligible/PossibleLAN/LikelyLAN/PossibleRemote
• MeasurementQuality - Excellent/Good/Poor/TooNoisy

Primitives

• Vector9 / Matrix9 - Fixed-size linear algebra types
• TimingSample - Tagged timing measurement
• AttackerModel - Threat model presets

Example

use timing_oracle_core::{
    statistics::{bootstrap_covariance_matrix, compute_deciles},
    analysis::bayes::compute_posterior_probability,
    types::{Vector9, AttackerModel},
};

// Compute deciles from timing samples
let baseline_deciles: Vector9 = compute_deciles(&baseline_times);
let sample_deciles: Vector9 = compute_deciles(&sample_times);
let delta = sample_deciles - baseline_deciles;

// Bootstrap covariance matrix
let cov = bootstrap_covariance_matrix(&baseline_times, &sample_times, 2000);

// Compute posterior probability of timing leak
let threshold = AttackerModel::AdjacentNetwork.threshold_ns();
let p_leak = compute_posterior_probability(&delta, &cov, threshold);

Documentation

• API Documentation
• Main crate
• GitHub Repository

License

MPL-2.0