Distributed Random Number Generation for Gaming Networks
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Solution Overview
Problem
Current random number generation algorithms used in gaming machines, such as pseudo-random number generators (PRNGs), face challenges in ensuring fairness and security, particularly in distributed networked gaming systems, where the need for secure and fair random numbers is critical to meet legal requirements and provide reliable outcomes for modern slot machine games.
Innovation Solution
A distributed random number generation system where multiple networked machines cooperate to perform partial calculations of RNG algorithms, with a master machine tracking the state and seed values, allowing for secure and fair random number generation across the network, utilizing techniques like linear congruential and cellular automata-based algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single machine runs the complete RNG algorithm, then the calculation is simple and fast, but the security and fairness are compromised in networked gaming systems
Solution Approach 1:
The RNG algorithm is divided into multiple independent calculation stages distributed across different machines in the network. Each machine performs a portion of the calculation using segmented functions, with results passed to the next machine. This segmentation prevents any single machine from controlling the entire RNG process, thereby improving security and fairness while maintaining manageable complexity through modular design.
2Reliability
If multiple machines are used to perform partial RNG calculations, then security and fairness are improved, but the system complexity increases
Solution Approach 1:
The system implements feedback mechanisms where each machine receives validation information from previous machines and provides results to subsequent machines. The distributed RNG process includes feedback loops for error checking, result verification, and state synchronization. This structured feedback approach coordinates the complex multi-machine process while ensuring the security and fairness improvements are maintained.
Solution Approach 2:
Each machine in the distributed RNG system is designed with multi-functionality, capable of performing various stages of the RNG algorithm, validating results from other machines, and communicating with any other machine in the network. This universal design reduces overall system complexity by allowing any machine to fulfill multiple roles rather than requiring specialized dedicated functions for each machine.
3Reliability
If distributed RNG calculation is implemented across multiple machines, then the randomness and reliability of outcomes are enhanced, but the calculation time and coordination overhead increase
Solution Approach 1:
The system performs preliminary actions by pre-establishing communication channels between machines, pre-validating algorithm parameters, and pre-synchronizing system states before the actual RNG calculation begins. This preliminary preparation reduces the time required during the actual distributed calculation process by eliminating the need for extensive coordination and validation during the critical RNG generation phase.
Data Source
AI summary
Methods, apparatus, and program products are disclosed for providing distributed RNG calculation capability. Generally, gaming machines cooperate on a gaming network to calculate a result for a RNG algorithm. A preferred system uses peer machines to perform partial RNG calculations, but cooperating server machines may also be used. One method calculates a first partial RNG calculation at a first machine using a seed value. The first machine transmits results of the first partial calculation to a second machine, which completes the RNG calculation. Some algorithms may include a step of combining partial results from two or more gaming machines. A preferred system uses a RNG state tracker and a seed tracker operating on a RNG master machine. This machine initializes a partial RNG with a seed value, and then tracks the state of the partial RNG using results from the completed calculation obtained over the network.