Dice game outcomes depend entirely on random number generation, determining whether players win or lose based on roll results. Generation models must produce unpredictable values, preventing manipulation while remaining verifiable for fairness confirmation. Tether-based dice platforms implement cryptographic randomness systems, balancing security requirements against computational efficiency. These models establish trust frameworks where players verify outcome legitimacy independently without relying solely on platform honesty.
https://crypto.games/dice/tether employs provably fair algorithms generating dice results through cryptographic operations combining platform and player inputs. The generation architecture prevents either party from predicting or manipulating outcomes unilaterally. Players receive tools verifying that displayed results are genuinely derived from committed seeds rather than arbitrary values selected after observing wagers. This transparency differentiates blockchain dice from traditional online implementations, where players must trust operators completely regarding randomness integrity.
Provably fair frameworks
Cryptographic commitment schemes form the foundation of verifiable randomness in dice games. Platforms generate server seeds before players place bets, then publish cryptographic hashes of these seeds publicly. Hash functions create fixed-length outputs from variable inputs while maintaining computational infeasibility of reverse-engineering original values from hashes. Players see hashed server seeds before betting, establishing that platforms committed to specific seeds are unable to change after observing wager parameters. Client seeds add player-contributed randomness, preventing platforms from pre-calculating advantageous outcomes.
The combination of server seeds, client seeds, and incrementing nonce values feeds into deterministic algorithms, producing final dice results. After bets resolve, platforms reveal unhashed server seeds, letting players verify they match the originally published hashes. Any mismatch proves that manipulation occurred since honest operations produce perfect hash alignment. This verification process remains accessible to anyone with a basic understanding of hash function operations, requiring no specialised technical expertise.
Hash function applications
HMAC-SHA256 algorithms generate dice outcomes from seed combinations. The function accepts server seeds as keys and combines client seeds with nonce values as messages. HMAC processing produces 256-bit output hashes that get converted to integers through hexadecimal interpretation. Modulo operations map these integers into 0-10000 ranges before division by 100 produces final results with two decimal precision. This mathematical transformation ensures uniform distribution across possible outcomes.
Every roll value between 0.00 and 99.99 possesses an equal probability of occurrence when generation follows proper HMAC-SHA256 procedures. The deterministic nature means identical seed combinations always produce similar results. Players can replicate calculations independently using publicly available cryptographic libraries. Online verification tools accept server seeds, client seeds, and nonces as inputs, then compute expected outcomes that must match platform-displayed results exactly.
Result conversion processes
Raw hash outputs require transformation into usable dice numbers. The conversion begins with interpreting hexadecimal hash strings as large integers. These integers typically exceed trillions, making direct usage impractical for 0-100 dice ranges. Modulo arithmetic reduces values to manageable sizes through remainder operations after division by 10000. The resulting remainders span 0-9999 uniformly distributed across this range. Final division by 100 produces decimal values matching standard dice result formats.
Conversion integrity depends on proper mathematical implementation without rounding errors or precision loss:
- Hexadecimal strings parse correctly into integer representations
- Modulo operations execute using appropriate data types, preventing overflow
- Division maintains two decimal place precision consistently
- Edge cases like maximum and minimum values are handled correctly
- Conversion reproducibility allows independent verification attempts
Platform implementations must document exact conversion procedures enabling players to replicate calculations accurately.