True Randomness from Big Data | |
Papakonstantinou, Periklis A.1; Woodruff, David P.2; Yang, Guang3 | |
刊名 | SCIENTIFIC REPORTS |
2016-09-26 | |
卷号 | 6页码:8 |
ISSN号 | 2045-2322 |
DOI | 10.1038/srep33740 |
英文摘要 | Generating random bits is a difficult task, which is important for physical systems simulation, cryptography, and many applications that rely on high-quality random bits. Our contribution is to show how to generate provably random bits from uncertain events whose outcomes are routinely recorded in the form of massive data sets. These include scientific data sets, such as in astronomics, genomics, as well as data produced by individuals, such as internet search logs, sensor networks, and social network feeds. We view the generation of such data as the sampling process from a big source, which is a random variable of size at least a few gigabytes. Our view initiates the study of big sources in the randomness extraction literature. Previous approaches for big sources rely on statistical assumptions about the samples. We introduce a general method that provably extracts almost-uniform random bits from big sources and extensively validate it empirically on real data sets. The experimental findings indicate that our method is efficient enough to handle large enough sources, while previous extractor constructions are not efficient enough to be practical. Quality-wise, our method at least matches quantum randomness expanders and classical world empirical extractors as measured by standardized tests. |
资助项目 | National Natural Science Foundation of China[61222202] ; National Natural Science Foundation of China[61433014] ; National Natural Science Foundation of China[61502449] ; National Natural Science Foundation of China[61602440] ; China National Program |
WOS研究方向 | Science & Technology - Other Topics |
语种 | 英语 |
出版者 | NATURE PUBLISHING GROUP |
WOS记录号 | WOS:000384416200001 |
内容类型 | 期刊论文 |
源URL | [http://119.78.100.204/handle/2XEOYT63/8140] |
专题 | 中国科学院计算技术研究所期刊论文_英文 |
通讯作者 | Woodruff, David P. |
作者单位 | 1.Rutgers State Univ, MSIS, Piscataway, NJ 08853 USA 2.IBM Res Almaden, San Jose, CA 95120 USA 3.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China |
推荐引用方式 GB/T 7714 | Papakonstantinou, Periklis A.,Woodruff, David P.,Yang, Guang. True Randomness from Big Data[J]. SCIENTIFIC REPORTS,2016,6:8. |
APA | Papakonstantinou, Periklis A.,Woodruff, David P.,&Yang, Guang.(2016).True Randomness from Big Data.SCIENTIFIC REPORTS,6,8. |
MLA | Papakonstantinou, Periklis A.,et al."True Randomness from Big Data".SCIENTIFIC REPORTS 6(2016):8. |
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