CasinArena GLS/MGA RNG Compliance

Transcription

1 Gambling Commission approved Test House Accredited to ISO/IEC 17025:2005 NMi Metrology & Gaming Ltd Parc Menai Bangor Gwynedd LL57 4EZ United Kingdom Tel: +44 (0) Report to Scientific Games Interactive CasinArena GLS/MGA RNG Compliance Report Reference ID Jurisdiction Issue Date NMI/136/027/UK/RTS/02 United Kingdom 30/10/2015 Executive Summary This report summarises our assessment of Scientific Games Interactive's Random Number Generator (RNG). SGI provided source code and a test harness for empirical testing. Our assessment methods included empirical testing of the RNG outputs and review of the supplied source code. Testing was performed on the following baseline: Platform Supplier: WMS Industries Inc. Platform Version: Ocelot CasinArena system v MGA v GLS v The RNG has been assessed for compliance with the relevant sections of the United Kingdom Gambling Commission (UKGC) Remote gambling and software technical standards (July 2015). The RNG outputs were determined to be acceptably random for the ranges assessed and also from a bitwise perspective. The source code for the RNG has been reviewed and no potential issues have been noted. Based on the selection of tests executed it is our assessment that the RNG is suitable for production of random numbers for gaming applications. No issues are raised. This report supersedes report NMI/136/027/UK/RTS/01 Authorised By Aled Hughes Laboratory Manager. Disclaimer This report and any accompanying documents are provided as is with no warranties. All systems may contain defects and nothing in this document is intended to represent or warrant that any items assessed are complete and free from errors. The operator remains solely responsible for the design, functionality and provision of their product(s) and service(s), including any liability arising from legal infringement, technical non-compliance or product warranty. This document remains the property of NMi Metrology & Gaming Ltd and, apart from supply to the intended regulator, is not to be copied, shared or distributed in any way without the express consent of NMi Metrology & Gaming Ltd. Page 1 of 10

2 Table of Contents Introduction... 3 Caveats...3 Quality Control...3 Test Item Details...4 Critical Components...4 Source Code...4 Testing Overview... 5 Customer Contacts...5 Dates... 5 Locations... 5 Applicable Standards... 5 Methods... 5 Code Analysis... 6 RNG Predictability...6 Seeding...6 Background Cycling...6 Outputs... 6 Period... 6 Mapping and Scaling... 6 RNG Failure... 6 Empirical Testing...7 Ranges & Volumes Tested...7 Appendix A: Requirements Met... 9 Appendix B: Requirements Not Applicable Page 2 of 10

3 Introduction NMi UK is approved to provide testing services relating to online gaming by the UK Gambling Commission, the Alderney Gambling Control Commission (AGCC), the Isle of Man Gambling Supervision Commission (GSC), the Jersey Gambling Commission, the Spanish National Gambling Commission (CNJ), the Government of Gibraltar Licencing Authority, the Malta Lotteries and Gaming Authority (LGA), Loto-Quebec and the British Columbia Gaming Policy and Enforcement Branch (GPEB). For a full list of NMi's accreditations (including details of all land-based and i-gaming regulatory approvals), please see Caveats The results presented in this document are a summary of the testing work undertaken, and this report is subject to a number of caveats, including: All items provided for inspection and/or testing are declared by the customer to be configured identically to those in commercial use. All software and source code provided for empirical testing and/or code review is declared by the customer to behave identically to the software and code in commercial use. Decisions taken by the supplied software in automatic test modes / simulators are reasonable emulations of those that would be expected to be taken by real players. All efforts have been taken to ensure that the testing undertaken has been as exhaustive as necessary to demonstrate compliance or non-compliance. NMi UK takes on trust that all test items (including all hardware and software), all documentation and all communications are accurate, truthful, and that there is no intention to deceive or subvert the assessment of compliance. Quality Control The monitoring of this testing project was the responsibility of NMi's Quality Manager and every effort has been made to ensure the accuracy of the information contained in this report. If errors or omissions are discovered, please contact us with details as soon as possible. NMi reserves the right to revise and reissue this Test Report if additional information is presented or discovered. Page 3 of 10

4 Test Item Details Critical Components GLS SHA-1 checksum c e55cd09dfa10794a0462fb File name gls/casinarena/spms/rng_r250/_rng.dll MGA SHA-1 checksum 45f4fa75363be90ec5d70db88ee84cf34e3854d3 File name Providers/_Rng.dll Source Code SHA-1 checksum d2528d4f05443ff c58fb3e5279dd7e5 9a49a7e796f754a30ee8a2172b94674ccf96f2bb acd2c2dc2db93a4723f7598bd6c2fd8eed93096b 4ce703a5f29f24a1b4fb5e34d32e903ad90c9510 f3fc2642bde85940ad9d ade47007d88b 8b4f8b916a2847f53da9aede6d11aac3af9ea0a9 c9ce9e1510eecb100975ccaead625c85c2cace7a File name 3rdparty/r250c/r250.cpp 3rdparty/r250c/r250.h 3rdparty/r250c/randlcg.cpp 3rdparty/r250c/randlcg.h WmsRng.cpp WmsRng.h main.cpp Page 4 of 10

5 Testing Overview Customer Contacts The customer liaisons were Lyndon Bennett and Ed Cudanes. Dates Testing was undertaken during the following periods: 18/09/ /09/2014 (including re-submissions) 30/10/ /10/2015 (signature update) Locations Testing was undertaken at the following locations: 1-3 Llys Helyg, Parc Menai, Bangor LL57 4EZ, UK Lougheed Highway, Burnaby, British Columbia, V5C 0E4, Canada. Applicable Standards Conformance with the following standards has been assessed, under the terms of NMi UK's ISO/IEC 17025:2005 accreditation: Document Remote Gambling and Software Technical Standards (July 2015) Abbreviation Used UK_RTS Methods Our assessment methods included statistical analysis of RNG outputs and source code review. Page 5 of 10

6 Code Analysis The submission consisted of C++ source files, implementing the RNG algorithm and supporting functions. The RNG uses an implementation of the r250 shift-register algorithm; 7 instances are used in combination. RNG Predictability The implementation of the RNG ensures that, even with knowledge of the algorithm, implementation and seed it would be computationally infeasible to predict the next number. Seeding Each of the RNG instances are seeded individually from an implementation of Park and Miller's linear congruential generator (LCG) RNG. The entropy source is the number of seconds elapsed since the Epoch (January 1, 1970). The RNG instances are only re-seeded on restart. Background Cycling The instances cycle randomly, at an acceptable rate to prevent prediction. Outputs The RNG produces integer numbers in the interval [0, 2^32]. Period The period of the RNG is sufficiently large to ensure that all game outcome combinations are possible for the declared applications. Mapping and Scaling The scaling algorithm used to reduce the range of the RNG maintains a uniform probability for the reduced range; therefore, the numbers within the lower range are equally probable. RNG Failure The RNG is required for system operation, so any failure to instantiate the RNG will cause a corresponding failure of the system itself to operate. In the event of failure, an alert in the Self-Monitoring program occurs. Page 6 of 10

7 Empirical Testing Ranges & Volumes Tested The following samples were generated for the RNG: 3 sets of 6 million 16 bit integers between 0 and 65,535 (inclusive) 20 million scaled integers between 0 to 5 (inclusive) 20 million scaled integers between 0 to 11 (inclusive) 20 million scaled integers between 0 to 36 (inclusive) 20 million scaled integers between 0 to 51 (inclusive) 20 million scaled integers between 0 to 74 (inclusive) 20 million scaled integers between 0 to 89 (inclusive) 20 million scaled integers between 0 to 255 (inclusive) 20 million scaled integers between 0 to 999 (inclusive) 20 million scaled integers between 0 to 9,999 (inclusive) 20 million scaled integers between 0 to 99,999 (inclusive) The results are summarised as follows: Analysis of 3 sets of 6 million 16 bit integers between 0 and 65,535 (inclusive) The numbers passed the Diehard Battery of tests, confirming that the software RNG is functioning correctly from a bitwise randomness perspective. Analysis of 20 million scaled integers between 0 and 5 (inclusive) Analysis of 20 million scaled integers between 0 and 11 (inclusive) Analysis of 20 million scaled integers between 0 and 36 (inclusive) Analysis of 20 million scaled integers between 0 and 51 (inclusive) Analysis of 20 million scaled integers between 0 and 74 (inclusive) Page 7 of 10

8 Analysis of 20 million scaled integers between 0 and 89 (inclusive) Analysis of 20 million scaled integers between 0 and 255 (inclusive) Analysis of 20 million scaled integers between 0 and 999 (inclusive) Analysis of 20 million scaled integers between 0 and 9,999 (inclusive) Analysis of 20 million scaled integers between 0 and 99,999 (inclusive) Page 8 of 10

9 Appendix A: Requirements Met UK_RTS / RTS - 7 Generation of random outcomes (3.7:1, 3.7A:1) To ensure that games and other virtual events operate fairly. Random number generation and game results must be acceptably random. Acceptably random here means that it is possible to demonstrate to a high degree of confidence that the output of the RNG, game, lottery and virtual event outcomes are random, through, for example, statistical analysis using generally accepted tests and methods of analysis. Adaptive behaviour (i.e. a compensated game) is not permitted. The RNG was determined to be acceptably random, unpredictable, and not reproducible for all of the ranges assessed. No evidence of compensatory or adaptive behaviour was observed in the RNG source code supplied. UK_RTS / RTS - 7 Generation of random outcomes (3.7A.a, 3.7A.a.i) a. RNG s should be capable of demonstrating the following qualities: i. the output from the RNG is uniformly distributed over the entire output range and game, lottery, or virtual event outcomes are distributed in accordance with the expected/theoretical probabilities The output from the RNG passed applicable empirical tests for randomness. UK_RTS / RTS - 7 Generation of random outcomes (3.7A.a, 3.7A.a.ii) a. RNG s should be capable of demonstrating the following qualities: ii. the output of the RNG, game, lottery, and virtual event outcomes should be unpredictable, for example, for a software RNG it should be computationally infeasible to predict what the next number will be without complete knowledge of the algorithm and seed value It would be computationally infeasible to predict what the next number will be without complete knowledge of the algorithm and seed value. UK_RTS / RTS - 7 Generation of random outcomes (3.7A.a, 3.7A.a.iii) a. RNG s should be capable of demonstrating the following qualities: iii. random number generation does not reproduce the same output stream (cycle), and that two instances of a RNG do not produce the same stream as each other (synchronise) Statistical analysis of the numbers drawn has demonstrated that the numbers are statistically independent. UK_RTS / RTS - 7 Generation of random outcomes (3.7A.a, 3.7A.a.iv) a. RNG s should be capable of demonstrating the following qualities: iv. any forms of seeding and re-seeding used do not introduce predictability The RNG threads are seeded individually by an implementation of the Park and Miller LCG RNG. The RNG instances are only reseeded if the IGS is restarted. These methods do not introduce predictability. Page 9 of 10

10 Appendix B: Requirements Not Applicable UK_RTS / RTS - 7 Generation of random outcomes (3.7:1, 3.7A:2) To ensure that games and other virtual events operate fairly. Where lotteries use the outcome of other events external to the lottery, to determine the result of the lottery (for example, using numbers from the National Lottery) the outcome must be unpredictable and externally verifiable. External events are not used. UK_RTS / RTS - 7 Generation of random outcomes (3.7A.a, 3.7A.a.v) a. RNG s should be capable of demonstrating the following qualities: v. any scaling applied to the output of the random number generator maintains the qualities above. This was a test of the RNG only. UK_RTS / RTS - 7 Generation of random outcomes (3.7A.b, 3.7A.b.i, 3.7A.b.ii, 3.7A.b.iii) b. For lotteries using external events - where it is not practical to demonstrate 7a. - the events outcomes should be: i. unpredictable, that is, events should be selected only where they may reasonably be assumed to be random events ii. unable to be influenced by the lottery operator (or external lottery manager) iii. publicly available and externally verifiable, for example, events that are published in local or national press would be acceptable. External events are not used. UK_RTS / RTS - 7 Generation of random outcomes (3.7A.c, 3.7A.c.i, 3.7A.c.ii, 3.7A.c.iii) c. For games or virtual events that use the laws of physics to generate the outcome of the game (mechanical RNGs), the mechanical RNG used should be capable of meeting the requirements in a. where applicable and in addition: i. the mechanical pieces should be constructed of materials to prevent decomposition of any component over time (e.g. a ball shall not disintegrate) ii. the properties of physical items used to choose the selection should not be altered iii. players should not have the ability to interact with, come into physical contact with, or manipulate the mechanics of the game. This was not a test of a mechanical RNG. END OF REPORT Page 10 of 10