Cryptographic infrastructure that makes streaming fraud mathematically impossible — not just detectable.
H33 Products · Streaming Fraud Prevention
H33 Streaming Fraud Prevention is a product that stops fake streams, bot farms, fake artist accounts, and AI-generated content fraud on streaming platforms. It owns the streaming-fraud-prevention workflow, the platform outcome, and the operational experience — and it composes H33 mechanisms rather than reimplementing them.
The product boundary. Streaming Fraud Prevention owns the streaming-fraud-prevention workflow and outcome. It uses H33 mechanisms for privacy, governance, evidence, monitoring, and verification; it does not redefine or own those mechanisms.
Fully homomorphic encryption powers encrypted biometric identity and encrypted content classification, so the platform never sees raw biometrics or audio. The product consumes FHE; it does not define or implement it.
Verification and ZK (STARK) proofs back device attestation and let any party confirm a stream's authenticity. The product surfaces the proofs; the verifying is owned elsewhere.
H33-74 supplies the portable 74-byte post-quantum attestation committed for each stream and upload. The product attaches attestations; it does not produce the attestation primitive.
Agent-008 governs any automated decisioning that acts on fraud signals, preserving authority and preventing drift. The product is governed by it; it does not perform governance.
HATS provides continuous control monitoring and claim-grade evidence over the deployment. The product is monitored by HATS; it does not record or score its own controls.
If Streaming Fraud Prevention swapped FHE for another confidential-computing technology, or STARK attestation for another proof system, it would still be the same product. The four-layer workflow and outcome are what it owns.
Bot farms. Fake accounts. Artificial plays. Click farms running thousands of headless browsers 24/7. The scale is industrial.
Current defenses rely on behavioral heuristics, post-event analysis, and pattern matching. All of them share the same fatal flaw: they detect fraud after it happens. And all of them are bypassable.
Heuristics can be mimicked. Patterns can be randomized. Post-event analysis means the damage is already done.
Detection is not prevention. The industry needs prevention.
Behavioral analysis. IP reputation. Listening-time heuristics. All reactive. All bypassable. Fraud farms adapt faster than detection models update.
Cryptographic device attestation. Encrypted biometric identity. Proof-of-work bot prevention. Encrypted content classification. Four layers of prevention, not detection.
Not detection. Prevention. Each layer makes a different class of fraud mathematically impossible.
Every stream gets a 192-byte STARK proof bound to a real device. Hardware fingerprint, network jurisdiction, endpoint integrity. Verified in under 1 microsecond.
Bot farms running headless browsers cannot produce valid proofs. The proof requires a physical device with verified hardware state. 16ms to generate. Fits in an HTTP header.
Per-stream proof · 16ms generation · <1µs verification
Artist identity verified through FHE-encrypted biometrics. The platform never sees the raw biometric data. The system returns a cryptographic yes or no.
Cannot create fake artist accounts when each requires a verified unique human. No synthetic identities. No duplicate registrations. One real person per artist profile.
Encrypted biometric throughput · 35.25µs per auth
Proof-of-work challenge on every play request. Invisible to real users on real devices. Bots burn compute at scale — economically unviable.
The cost to fake one stream exceeds the revenue from that stream. At bot-farm scale, the economics collapse entirely. A single script tag deploys it.
Challenge-response for real devices · Invisible to users
Encrypted ML classifies whether audio is AI-generated versus human-performed. The model runs on ciphertext. The platform never accesses raw audio.
Privacy-preserving detection of synthetic content. No raw audio leaves the encryption boundary. The classification result is a signed attestation, not an opinion.
Classification on encrypted audio · Zero plaintext exposure
No platform rebuild required. H33 fits into existing infrastructure.
Every stream, upload, and play event produces an H33-74 attestation — 74 bytes, post-quantum signed, independently verifiable. Three independent hardness assumptions: MLWE lattices, NTRU lattices, and hash-based signatures.
Labels, distributors, auditors, and regulators can verify any stream's authenticity without trusting the platform. The proof is mathematical, not behavioral. It does not degrade. It does not have false positives.
Any party can verify. Labels do not need to trust the platform. Distributors do not need to trust the label. Regulators do not need to trust anyone. The cryptographic proof is self-verifying. 10 patents pending. 300+ claims.
Production numbers. Sustained. Independently reproducible.
| Component | Latency | Detail |
|---|---|---|
| DeviceProof generation | 16ms | 192-byte STARK proof bound to device hardware |
| DeviceProof verification | <1µs | Server-side, fits in request pipeline |
| Biometric auth | 35.25µs | 2.29M auth/sec sustained · FHE-encrypted |
| BotShield challenge | Sub-ms | Proof-of-work · invisible to real users |
| H33-74 attestation | 42µs | 74 bytes · post-quantum signed · per stream |
Zero user-perceived latency. DeviceProof generates while the page loads. BotShield runs in the background. Biometric verification happens once at upload. H33-74 attestation commits after the stream starts. The listener notices nothing.
Cannot produce valid DeviceProof. Headless browsers lack hardware attestation state. Every fake stream is rejected before it plays.
Cannot pass encrypted biometric verification. One verified human per artist profile. Synthetic identities are cryptographically impossible.
BotShield proof-of-work makes bulk streaming economically unviable. The compute cost to fake streams exceeds the revenue they generate.
Encrypted ML classification detects synthetic audio without accessing the raw content. The platform cannot be used to launder AI-generated music as human-performed.
Use H33 Streaming Fraud Prevention when you run a streaming or media platform facing fake streams, bot farms, fake artist accounts, or AI-generated content fraud, and you want prevention at the point of play and upload rather than after-the-fact analytics. Status is honest: DeviceProof, BotShield, biometric verification, and the encrypted classification layer are the deployable pieces; the performance figures are measured production numbers reproducible on /benchmarks/, and the illustrative "660,000 fake streams" figure describes the problem scale, not a per-customer measurement.
For single-institution authentication fraud — deepfakes, synthetic identities, credential stuffing at the login boundary — don't use this; use AI Fraud Detection instead. For cross-institutional bank fraud rings that cannot share data, use FraudShield instead.
To evaluate a mechanism itself, read the FHE hub for privacy, Verification and ZK for the proof layer, or H33-74 for the portable attestation primitive.
Streaming fraud prevention, and how the product composes H33 mechanisms.
A product that stops fake streams, bot farms, fake artist accounts, and AI-generated content fraud on streaming platforms. It combines cryptographic device attestation, FHE-encrypted biometric identity, proof-of-work bot prevention, and encrypted content classification into a four-layer prevention workflow. It owns the streaming-fraud-prevention workflow and outcome; it uses H33 mechanisms for privacy, evidence, verification, governance, and monitoring rather than reimplementing them.
It USES FHE for encrypted biometric identity and content classification, USES Verification and ZK (STARK) proofs for device attestation and per-stream verifiability, and USES H33-74 to commit a portable post-quantum attestation for each stream. Automated decisioning is GOVERNED_BY Agent-008, and the deployment is MONITORED_BY HATS. The product composes these mechanisms; it does not own or redefine them.
Yes. If it swapped FHE for another confidential-computing technology, or STARK attestation for another proof system, it would still be the same product. The four-layer streaming-fraud-prevention workflow and the platform outcome are what it owns. Mechanisms are chosen, not owned.
No. Artist biometric identity is verified under FHE encryption, and content classification runs on ciphertext, so the platform receives cryptographic results rather than raw biometric or audio data. The privacy mechanism that makes this possible is FHE, which the product uses but does not define.
Use it for streaming and media platforms facing fake streams, bot farms, fake artist accounts, or AI-generated content fraud. For single-institution authentication fraud such as deepfakes and credential stuffing, use AI Fraud Detection instead. For cross-institutional bank fraud rings, use FraudShield instead.