With 32 worker threads, Dodge-mimic anonymity-set bandwidth overhead (as percentage of undefended video content) ranges from 21.0%±0.8 at k=2 to 73.1%±2.8 at k=10 and 107.4%±1.5 at k=20; beyond k=10–16 threads, additional parallelism yields diminishing returns and can increase overhead due to thread-boundary fragmentation of the optimal search space.

Dodge-mimic reduces vRF deep-learning classifier accuracy to 10.6–15.6% for k=10 anonymity sets, matching the theoretical k-anonymity bound of (100/k)%=10%. The heuristic Walls Have Ears attack also fails: WHE-seg peaks at ≤8.5% and WHE-cyk at 9.7%, confirming that equalized cycle sizes leak no per-video information.

§5.2, Table 2 defense

In live deployment with k=10 anonymity sets, video+audio+ABR, Dodge-mimic causes zero playback stalls in approximately 68% of sessions; for the remaining 32%, the mean initial stall time is 1.6 seconds — an extension of normal startup delay rather than mid-playback interruption — and all cycles complete roughly 20 seconds before playback ends even with large trailing cycles.

§5.3, §5.3.3 evaluation

Small random per-session perturbations (±bytes per range) to Dodge-mimic extended manifests fully compensate for HPACK header compression in HTTP/2, yielding vRF accuracy of 10.5–14.2% for k=10 — indistinguishable from HTTP/1.1 results and near the theoretical bound. Because HTTP/3-QUIC uses analogous QPACK compression, the approach generalizes to the dominant future transport.

§5.4, Table 3 defense

Even with perfectly equalized cycle sizes, a persistent intra-response timing side channel survives in Dodge: timing characteristics of individual packets within server responses cannot be controlled client-side, causing vRF accuracy to remain slightly above random guessing in all tested scenarios. This residual leak is consistent and observable regardless of anonymity set size or media configuration.

§5.2.1, §6 detection

Encrypted DNS (DoH, DoT) and Encrypted ClientHello (ECH) fully hide plaintext domain names from on-path observers but leave destination IP addresses visible, enabling IP-based fingerprinting with no protocol-level metadata required. An adversary collecting only NetFlow/IPFIX destination-IP records—without TLS fingerprints, packet timing, or port metadata—correctly identifies 72% of all 520K dual-stack websites over IPv4 and 68% over IPv6.

2026-ahmad-more-space-less §1, §3, §6.1 website-fingerprintsni-blockingesni-eh-blocking

IP-based fingerprints are operationally stable: approximately 60% of domains exhibit no IP changes across the three-month measurement period for both IPv4 and IPv6. Among churning domains, per-domain churn behavior is comparable across protocols—about 28% of churning IPv4 domains rotate within one hour on average vs 35% for IPv6. Fingerprint databases of ≈100 KB per site (50–60 GB for 520K sites) can be maintained with only periodic DNS re-resolution at negligible overhead for adversaries already collecting NetFlow data.

2026-ahmad-more-space-less §6.4, §5.3.2 website-fingerprinttraffic-shape

IP-based website fingerprinting achieves 93–94% top-1 accuracy on dual-stack incomplete websites (those with any IPv4-only third-party dependencies) using both IPv4 and IPv6 fingerprints across 228K sites. For dual-stack complete sites (all resources reachable over IPv6), accuracy drops to 56% over IPv4 and 45% over IPv6. The discriminating signal comes almost entirely from secondary resource IPs: using only the primary domain's IP yields 5–8% accuracy regardless of protocol.

2026-ahmad-more-space-less §6.1, Table 1 website-fingerprinttraffic-shape

IPv6 does not inherently worsen fingerprinting privacy: dual-stack complete sites are measurably harder to fingerprint via IPv6 (45%) than IPv4 (56%), contrary to community fears. The key driver is not the protocol but hosting provider behavior—Cloudflare, Google, and Fastly co-locate large clusters of domains behind a small set of shared IPv6 addresses, creating structured similarity clusters that reduce classifier separability, while Amazon allocates domains across vast unique IPv6 prefixes, increasing fingerprintability.

2026-ahmad-more-space-less §7.1, §6.1, §6.2 website-fingerprintip-blocking

Raw IPv6 address entropy is inflated by continuous discovery of previously-unseen addresses throughout the measurement period (1.04M distinct IPv6 vs 552K IPv4 addresses observed), giving a misleading impression of higher fingerprint discriminability. Realized entropy—computed per crawl-batch snapshot to exclude ephemeral addresses never seen during testing—shows more than 20% of IPv6 addresses provide less information gain than their IPv4 counterparts, explaining why dual-stack complete IPv6 fingerprinting accuracy (45%) underperforms IPv4 (56%) despite higher raw uniqueness.

2026-ahmad-more-space-less §6.3, Figure 6, Figure 7 website-fingerprint

Incoming traffic is the dominant website fingerprinting leakage vector in Nym because the network requester does not apply Poisson-rate cover traffic by default. The Features In feature set achieves 72.1% random-forest accuracy on Nym Testnet — exceeding Tor's 70.2% — while outgoing Features Out leaks only 43.9% (vs. 65.0% for Tor). The combined Features In/Out (70.0%) provides no additional information beyond Features In alone, confirming the inbound channel is the bottleneck. Enabling Poisson-rate cover traffic on the network requester side (lambda_QR = 10-20 ms) lowers the Tik-Tok F1 score from 0.87 to 0.75-0.64 but raises latency overhead to 7.32-12.66x (Table 4, configs 10-11).

2026-joll-s-website-fingerprinting-nym §5.1, Table 3 website-fingerprinttraffic-shape