2026-witwer-dodge-client-side-framework
findings extracted from this paper
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.
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.
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.
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.
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.