Improving User Privacy in Practical Quantum Private Query with Group Honesty Checking

IF 4.4 Q1 OPTICS
Chun-Yan Wei, Qing-Le Wang, Xiao-Qiu Cai, Tian-Yin Wang
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Abstract

Current cheat-sensitive security level of user privacy in quantum private query (QPQ) is far from meeting its ideal requirement. Dishonest database trying to elicit user privacy can only be (delayedly) detected after the finish of the protocol with merely a nonzero probability. Worse yet, no estimation of p s u c c $p_{succ}$ (i.e., the success probability of dishonest database's cheating) has been given till now. Such estimation is quite necessary because a significant p s u c c $p_{succ}$ means frangible user privacy especially in the cheat-sensitive security model. Here, p s u c c $p_{succ}$ of the first and best-known quantum-key-distribution (QKD)-based QPQ protocol proposed by Jakobi et al. is estimated, which shows that dishonest database can elicit user privacy with significant probability (e.g., as high as 42.8% for database size N = 10000 $N=10000$ ) while such cheating can only be (delayedly) detected with probability 50 %. Common strategy to improve user privacy, i.e., adding honesty checking to detect malicious database may hurt the privacy of the other party, i.e. database security. To solve this problem, a new group honesty checking is proposed, which will not hurt database security and can reduce $  p s u c c $p_{succ}$ to a very small value (e.g. 0.26% for database size 10000), thus assuring high user privacy (note that p s u c c $p_{succ}$ = 0 means ideal user privacy).

Abstract Image

利用群体诚信检查改善实用量子隐私查询中的用户隐私
当前量子私有查询(QPQ)中用户隐私的欺骗敏感安全水平远未达到理想要求。试图窃取用户隐私的不诚实数据库只能(延迟地)在协议结束后以非零概率检测到。更糟糕的是,目前还没有给出p succ $p_{succc}$的估计(即不诚实数据库欺骗成功的概率)。这样的估计是非常必要的,因为一个显著的p = c $p_{succc}$意味着脆弱的用户隐私,特别是在欺骗敏感的安全模型中。本文对Jakobi等人提出的第一个也是最著名的基于量子密钥分发(QKD)的QPQ协议的p succ $p_{suc}$进行了估计,这表明不诚实的数据库有很大的概率会泄露用户隐私(例如:高达42.8%的数据库大小N=10000$ N=10000$),而这种作弊只能(延迟地)以50%的概率被检测到。常用的提高用户隐私的策略,即增加诚实性检查来检测恶意数据库,可能会损害对方的隐私,即数据库安全。为了解决这个问题,提出了一种新的组诚实度检测方法,该方法在不损害数据库安全性的前提下,可以将$p_{suc}$降低到一个非常小的值(例如,对于数据库大小为10000的值,可以降低0.26%)。这样可以保证较高的用户隐私(注意,p succ $p_{succ}$ = 0表示理想的用户隐私)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
7.90
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