| Title | Highly Parallel Seedless Random Number Generation from Arbitrary Thread Schedule Reconstruction |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Aguilar, Eryn, Dancel, Jevis, Mamaud, Deysaree, Pirosch, Dorothy, Tavacoli, Farin, Zhan, Felix, Pearce, Robbie, Novack, Margaret, Keehu, Hokunani, Lowe, Benjamin, Zhan, Justin, Gewali, Laxmi, Oh, Paul |
| Conference Name | 2019 IEEE International Conference on Big Knowledge (ICBK) |
| Keywords | arbitrary thread schedule reconstruction, Blum-Elias algorithm, compare-and-swap operations, computerized data, cryptography, data sets, DieHarder, encryption keys, ENT, highly parallel seedless random number generation, Human Behavior, Metrics, multiprocessor, private data, PRNG, probability, pubcrawl, random key generation, random number generation, random number generator, Random sequences, random source, reconstruction algorithm, Resiliency, revenue source, Scalability, threading, trng, uniform probability outcomes, universal concern, unlimited parallelism |
| Abstract | Security is a universal concern across a multitude of sectors involved in the transfer and storage of computerized data. In the realm of cryptography, random number generators (RNGs) are integral to the creation of encryption keys that protect private data, and the production of uniform probability outcomes is a revenue source for certain enterprises (most notably the casino industry). Arbitrary thread schedule reconstruction of compare-and-swap operations is used to generate input traces for the Blum-Elias algorithm as a method for constructing random sequences, provided the compare-and-swap operations avoid cache locality. Threads accessing shared memory at the memory controller is a true random source which can be polled indirectly through our algorithm with unlimited parallelism. A theoretical and experimental analysis of the observation and reconstruction algorithm are considered. The quality of the random number generator is experimentally analyzed using two standard test suites, DieHarder and ENT, on three data sets. |
| DOI | 10.1109/ICBK.2019.00009 |
| Citation Key | aguilar_highly_2019 |
Highly Parallel Seedless Random Number Generation from Arbitrary Thread Schedule Reconstruction