2014
DOI: 10.1038/ncomms4732
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Finite-key analysis for measurement-device-independent quantum key distribution

Abstract: Quantum key distribution promises unconditionally secure communications. However, as practical devices tend to deviate from their specifications, the security of some practical systems is no longer valid. In particular, an adversary can exploit imperfect detectors to learn a large part of the secret key, even though the security proof claims otherwise. Recently, a practical approach-measurement-device-independent quantum key distribution-has been proposed to solve this problem. However, so far its security has… Show more

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Cited by 393 publications
(434 citation statements)
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“…[8] (see also [9] for a detailed analysis of this subject). We assume that the statistical fluctuations obey a Gaussian distribution [10].…”
Section: Finite Size Key Ratementioning
confidence: 99%
See 1 more Smart Citation
“…[8] (see also [9] for a detailed analysis of this subject). We assume that the statistical fluctuations obey a Gaussian distribution [10].…”
Section: Finite Size Key Ratementioning
confidence: 99%
“…
Security in quantum cryptography [1, 2] is continuously challenged by inventive attacks [3][4][5][6][7] targeting the real components of a cryptographic setup, and duly restored by new countermeasures [8][9][10] to foil them. Due to their high sensitivity and complex design, detectors are the most frequently attacked components.
…”
mentioning
confidence: 99%
“…Such an assumption is not necessarily justified when one considers a rigorous security proof. Recently, this Gaussian assumption was removed from the security proof by applying the Chernoff bound and the Hoeffding inequality [24,25]. We refer to this latter technique as the Chernoff-Hoeffding method.…”
Section: Introductionmentioning
confidence: 99%
“…Considering the statistical fluctuation, there exists a gap between e e b and e t p . Random sampling method provides a upper bound for this gap with a fixed failure probability ǫ [29,30], in our security analysis, ǫ = 10 −10 . Here, the Serfling inequality [31] is applied to estimate this gap.…”
Section: Appendix A: Phase Error Rate Estimationmentioning
confidence: 97%