Embedding explicit TTL values in mesh-routed messages leaks proximity information — a recipient can infer that a high-TTL message originator was recently nearby. MIRAGE mitigates this with memoryless TTLs: carriers independently discard messages with probability q per epoch, implementing a branching process with replication factor R ≤ nmax·(1−q). Setting q > 1 − 1/nmax ensures sub-critical message extinction with expected lifetime ≈ −ln(nmax)/ln(R) epochs.

MIRAGE's differentially private routing function provably bounds adversary inference: for a routing protocol satisfying ε-DP with ε = ln(4), any hypothesis test achieving a true positive rate of 80% necessarily incurs a false positive rate of at least 20%. The TPR-to-FPR ratio is bounded by e^ε for any ε-DP routing function, providing a formal privacy guarantee against routing-level statistical disclosure attacks.

§V–VI defense

MIRAGE delivers 15× more messages than random-walk protocols and significantly outperforms probabilistic flooding in delivery rate. On the pedestrian YJMob100K dataset at p=0.6, MIRAGE achieved a delivery rate of 36.9%, compared to 9.1% for probabilistic flooding (4.1×) and 3.2% for handoff (11.5×). MIRAGE incurs substantially lower network load than maximal flooding (86.9% delivery) while maintaining better delivery rates than all non-flooding baselines.

§VII-B evaluation

The PPBR (probabilistic profile-based routing) protocol leaks user community membership through observable routing decisions: in a controlled experiment with 800 majority and 200 minority users, a statistical disclosure attack achieved a true positive rate of 100% and false positive rate of 0% when identifying minority users. Even under a conservative PPBR configuration (top 1/3 fraction acceptance), the attack achieved 100% TPR and only 0.4% FPR.

§V detection

MIRAGE constructs a global mobility graph using locally differentially private per-user submissions, requiring only O(ln(|M|/β) / (α²ε²)) users to achieve per-edge accuracy α with probability 1−β. For a 100-district map with ε=0.05 and α=0.5, fewer than 1 million users suffice for top-2 district reporting; for top-3 districts the requirement drops to under 200K users.

§VI-B defense

A three-stage detection pipeline exploiting the "dual-role" behavioral fingerprint of single-IP circumvention relays achieved 23.2% recall (96/414 ground-truth relays) with a 0.18% false-positive rate against 97,651 benign TLS servers, for an overall accuracy of 99.5%. The ground-truth set covered OpenVPN, WireGuard, and SOCKS relays identified in a 17 TB single-day backbone trace (WIDE Project, April 9, 2025).

2026-almutairi-server §3.4, Table 1 traffic-shapedpiflow-correlation

The paper identifies a fundamental architectural vulnerability in single-IP circumvention designs: a relay must generate new observable flows (via DNS or TLS SNI) to reach end services after receiving client connections, creating a detectable server-and-client behavioral contrast. A relay accessing user-facing domains (news, social media) scores high on a Relay Suspicion Score (w=0.9) versus infrastructure domains (w=0.1). The paper argues this host-level signal is censorship-invariant and cannot be concealed by link obfuscation.

2026-almutairi-server §2.4, §4 traffic-shapedpisni-blocking

Simulations extending the ENEM19 game-theory framework show that ephemeral proxy schemes (modeled on Snowflake/Lantern) effectively neutralize both the "optimal" and "aggressive" censors from the original framework. In overprovisioned settings (proxies arriving at 250/step vs. 200 clients/step), even the null censor scenario outperforms either censor in equal-arrival settings. Over 90% of waiting users receive a proxy within 1 time step. The critical variable is not censor sophistication but proxy arrival rate relative to client demand—high proxy churn combined with high arrival rate defeats both enumeration strategies tested.

2026-fares-game §4, Fig 1, Fig 2, Table 1 dpitraffic-shape

The host-profiling censor (passive traffic analysis: count connections per server, block those exceeding a threshold τ within a window w) blocks essentially all circumvention user traffic within 30 time steps for all classifier qualities tested (ρ_TP ∈ {0.9, 0.95, 0.99}), while causing far less collateral damage than zig-zag (never exceeding ~30% innocent server blocking). Snowflake resists this attack well: with w=3, τ=3, over 94.48% of users receive a proxy within 2 steps even with worst-classifier rules, and final unblocked server rates are 91.24–99.04%. The host profiling approach is feasible for passive censors who cannot enumerate the distribution channel.

2026-fares-game §5.2, Table 2, Table 3 traffic-shapeml-classifier

Multi-censor simulations show that single-censor-optimized distribution strategies perform suboptimally in realistic multi-region deployments. When two networks have different censor strategies (e.g., one optimal, one zig-zag), the distributor cannot detect that a proxy is blocked until all censors have blocked it; this leaves clients without reachable proxies despite the proxy appearing "available" from the distributor's view. The authors conclude that "single-censor evaluation does not accurately predict more realistic deployment performance." A zig-zag censor in one region with 0.25 weight caused 44.4% collateral damage while reducing proxy lifetime to a median of 4 steps.

2026-fares-game §6, Table 4 traffic-shapeflow-correlation

The zig-zag traffic analysis attack (confirmed supported in Geedge TSG leak) rapidly enumerates all static proxy pools. With ζ_watch ∈ {4, 6} steps and a best-quality classifier (ρ_TP=0.99, ρ_FP=0.001), almost total proxy enumeration and user blockage occurs well before step 300. Even ζ_watch=2 leaves ~50% of users blocked. Collateral damage is high across all settings when ζ_watch ≥ 4: eventually ~50% of innocent servers are also blocked. However, Snowflake-style ephemeral proxies resist zig-zag effectively: reachability remains above 95% after 360 steps because churn prevents the censor from expanding its known proxy set beyond agents' direct assignments.

2026-fares-game §5.1, Fig 3, Fig 4 traffic-shapeflow-correlation