利用散列提高光子到达时间量子随机数发生器的效率

B. Solymos, L. Bacsardi
{"title":"利用散列提高光子到达时间量子随机数发生器的效率","authors":"B. Solymos, L. Bacsardi","doi":"10.1109/SACI58269.2023.10158613","DOIUrl":null,"url":null,"abstract":"Quantum random number generators can deliver entropy based on inherently non-deterministic physical phenomena, which is essential for applications where quality randomness is needed (e.g. in cryptography). Our generator based on photon time-of-arrival exploits the randomness of light emission in semiconductors. To measure the time between detections we use a continuous (non-restartable) clock for time tagging, which permits simpler hardware but produces slightly different output from the optimal exponential distribution. To handle this and produce quality output, we use Toeplitz hashing based bit generation after modeling and calculating a lower bound of extractable min-entropy for the general case of this generation scheme with a continuous clock. We produced uniformly distributed output bits with our setup, while also reaching a generation efficiency of 9.75 output bits per 16-bit input record, which is an improvement to our previously used method. Our generator has also been statistically tested using four of the popular statistical test suites.","PeriodicalId":339156,"journal":{"name":"2023 IEEE 17th International Symposium on Applied Computational Intelligence and Informatics (SACI)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Efficiency improvement of photon arrival time based quantum random number generator with hashing\",\"authors\":\"B. Solymos, L. Bacsardi\",\"doi\":\"10.1109/SACI58269.2023.10158613\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Quantum random number generators can deliver entropy based on inherently non-deterministic physical phenomena, which is essential for applications where quality randomness is needed (e.g. in cryptography). Our generator based on photon time-of-arrival exploits the randomness of light emission in semiconductors. To measure the time between detections we use a continuous (non-restartable) clock for time tagging, which permits simpler hardware but produces slightly different output from the optimal exponential distribution. To handle this and produce quality output, we use Toeplitz hashing based bit generation after modeling and calculating a lower bound of extractable min-entropy for the general case of this generation scheme with a continuous clock. We produced uniformly distributed output bits with our setup, while also reaching a generation efficiency of 9.75 output bits per 16-bit input record, which is an improvement to our previously used method. Our generator has also been statistically tested using four of the popular statistical test suites.\",\"PeriodicalId\":339156,\"journal\":{\"name\":\"2023 IEEE 17th International Symposium on Applied Computational Intelligence and Informatics (SACI)\",\"volume\":\"81 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-05-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 IEEE 17th International Symposium on Applied Computational Intelligence and Informatics (SACI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SACI58269.2023.10158613\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 IEEE 17th International Symposium on Applied Computational Intelligence and Informatics (SACI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SACI58269.2023.10158613","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

量子随机数生成器可以基于固有的不确定性物理现象提供熵,这对于需要高质量随机性的应用(例如在密码学中)至关重要。我们的光子到达时间发生器利用了半导体中光发射的随机性。为了测量检测之间的时间间隔,我们使用连续(不可重启)时钟进行时间标记,这允许更简单的硬件,但产生与最佳指数分布略有不同的输出。为了处理这个问题并产生高质量的输出,我们在对具有连续时钟的生成方案的一般情况建模和计算可提取最小熵的下界之后,使用基于Toeplitz哈希的位生成。通过我们的设置,我们产生了均匀分布的输出位,同时也达到了每16位输入记录9.75输出位的生成效率,这是对我们以前使用的方法的改进。我们的生成器还使用四种流行的统计测试套件进行了统计测试。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Efficiency improvement of photon arrival time based quantum random number generator with hashing
Quantum random number generators can deliver entropy based on inherently non-deterministic physical phenomena, which is essential for applications where quality randomness is needed (e.g. in cryptography). Our generator based on photon time-of-arrival exploits the randomness of light emission in semiconductors. To measure the time between detections we use a continuous (non-restartable) clock for time tagging, which permits simpler hardware but produces slightly different output from the optimal exponential distribution. To handle this and produce quality output, we use Toeplitz hashing based bit generation after modeling and calculating a lower bound of extractable min-entropy for the general case of this generation scheme with a continuous clock. We produced uniformly distributed output bits with our setup, while also reaching a generation efficiency of 9.75 output bits per 16-bit input record, which is an improvement to our previously used method. Our generator has also been statistically tested using four of the popular statistical test suites.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Application of 3D multi-user software tools in digital medicine – a scoping review Machine Learning in Heat Transfer: Taxonomy, Review and Evaluation Auction-Based Job Scheduling for Smart Manufacturing Safe trajectory design for indoor drones using reinforcement-learning-based methods Investigation of reward functions for controlling blood glucose level using reinforcement learning
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1