Approximately 60% of users in the 8,400-participant US dataset had a unique overall browser fingerprint when combining 13 standard attributes, matching FingerprintJS's advertised 60% accuracy. Fingerprinting risk followed strict monotonic trends: uniqueness increased with age (65+ group most at risk) and decreased with income (household income under $25,000 group at greatest risk), while males showed more unique overall fingerprints but females showed higher uniqueness on passive-fingerprintable attributes (User-Agent, Languages).

Simulating a shift from a 0% to 100% male dataset sample changes Shannon entropy estimates by more than 10% for User-Agent (downward) and more than 68% for WebGL Renderer (upward), revealing that prior large fingerprinting studies — Panopticlick (83.6–94.2% unique, predominantly reached via tech-oriented channels) and AmIUnique (90% desktop unique) — likely misrepresent real-world risk due to uncontrolled male bias, as confirmed by a directly comparable study showing 76.5% male participants.

§6, Figure 4 evaluation

A simple three-hidden-layer MLP trained on only 13 standard browser attributes achieves AUROC above 0.5 for every tested demographic group: gender 0.663–0.679, age 55+ 0.644, Hispanic ethnicity 0.60, Asian race 0.698, Black race 0.677, and high-income bracket 0.617. Because the model used only attributes already collected by mainstream fingerprinting scripts (e.g., FingerprintJS), richer real-world attribute sets would yield substantially higher demographic inference accuracy.

§7.1, Table 5 detection

User-Agent and Accept-Language browser attributes are transmitted in HTTP request headers, enabling passive server-side fingerprinting without JavaScript execution or any browser-detectable signal. In the 8,400-user dataset, the Languages attribute placed Hispanic users (who represent only 11% of the sample) among more than 45% of users with 'es-US' as their Languages value, substantially reducing their anonymity set size versus the general population.

§4.1, §6.2 detection

Screen resolution (572 distinct values, 4.5% unique, entropy 5.51), WebGL Unmasked Renderer (654 distinct values, 3.2% unique, entropy 6.833), and User-Agent (434 distinct values, 2.8% unique, entropy 4.613) are simultaneously the most uniquely identifying individual attributes and the strongest demographic predictors by normalized mutual information across all five demographic categories tested (gender, age, income, Hispanic ethnicity, race).

§7.2, Figure 6, Table 1 detection

AnyTLS implements a persistent idle-session pool with configurable parameters: idle_session_check_interval (default 30s), idle_session_timeout (default 30s), and min_idle_session (default 5). The client maintains at least 5 pre-established TLS sessions at all times to enable fast connection reuse without a new TLS handshake per request.

2026-anon-anytls-anytls-sing-box-2026 §2.4, §5.3 tls-fingerprinttraffic-shape

Compared to peer protocols, AnyTLS rates 'medium' performance (vs. VLESS 'high', Hysteria2 'very high', TUIC 'high'), uses TCP/TLS transport (vs. UDP/QUIC for Hysteria2 and TUIC), and relies on padding-based obfuscation vs. REALITY/WebSocket (VLESS) or HTTP/3 framing (Hysteria2). Client ecosystem support is currently limited primarily to sing-box, vs. broad cross-client support for VLESS, Trojan, and Hysteria2.

2026-anon-anytls-anytls-sing-box-2026 §1.5, Table 1.5 traffic-shapetls-fingerprint

AnyTLS is a TLS-based proxy protocol maintained by the sing-box team, designed in 2024 and first released in the sing-box dev-next branch. Its core mechanism wraps arbitrary proxy traffic in standard TLS and applies a configurable padding scheme (Padding Scheme) to enhance traffic concealment while maintaining compatibility with standard TLS infrastructure.

2026-anon-anytls-anytls-sing-box-2026 §1.1–1.3 tls-fingerprinttraffic-shapedpi

Russia's Ministry of Digital Development issued guidelines effective April 15, 2026 requiring popular apps to detect and restrict access from VPN-using devices. RKS Global's analysis of 30 popular Russian Android apps found that 22 of 30 implement VPN detection, and 19 of those transmit the detected VPN status to their servers. This represents a shift from network-layer blocking (TSPU) to app-layer enforcement as an additional censorship vector.

2026-rks-russian-apps-vpn-detection §3, §4 tls-fingerprintml-classifier

The RKS Global report documents a two-tier Russian censorship architecture: TSPU network-layer blocking (documented by Xue et al. 2024) at the ISP level, now supplemented by mandated app-layer VPN detection in the 30 most popular Russian Android apps. This layered approach means a circumvention tool that successfully bypasses TSPU at the network layer can still be detected and reported by the app layer, closing the gap that network-only circumvention leaves open.

2026-rks-russian-apps-vpn-detection §2, §7 tls-fingerprintml-classifiermiddlebox-interference

Obscura's browser-to-browser (B-B) WebRTC connections produce DTLS ClientHello and ServerHello messages indistinguishable from genuine browser traffic: across 100 captured handshakes compared against Facebook Messenger, Google Meet, Discord, and a reference WebRTC app using the dfind tool, no unique identifiers were found in C-C connections, and the sole Firefox-specific fingerprint (ServerHello length 86 bytes, cipher TLS_AES_128_GCM_SHA256, extension field length 46 bytes) matches the default Firefox WebRTC profile — meaning blocking it would also block all legitimate Firefox WebRTC users.

2026-vilalonga-obscura-enabling-ephemeral §5.2 tls-fingerprintdpi