14,236 research outputs found
Secure self-calibrating quantum random bit generator
Random bit generators (RBGs) are key components of a variety of information
processing applications ranging from simulations to cryptography. In
particular, cryptographic systems require "strong" RBGs that produce
high-entropy bit sequences, but traditional software pseudo-RBGs have very low
entropy content and therefore are relatively weak for cryptography. Hardware
RBGs yield entropy from chaotic or quantum physical systems and therefore are
expected to exhibit high entropy, but in current implementations their exact
entropy content is unknown. Here we report a quantum random bit generator
(QRBG) that harvests entropy by measuring single-photon and entangled
two-photon polarization states. We introduce and implement a quantum
tomographic method to measure a lower bound on the "min-entropy" of the system,
and we employ this value to distill a truly random bit sequence. This approach
is secure: even if an attacker takes control of the source of optical states, a
secure random sequence can be distilled.Comment: 5 pages, 2 figure
Postprocessing for quantum random number generators: entropy evaluation and randomness extraction
Quantum random-number generators (QRNGs) can offer a means to generate
information-theoretically provable random numbers, in principle. In practice,
unfortunately, the quantum randomness is inevitably mixed with classical
randomness due to classical noises. To distill this quantum randomness, one
needs to quantify the randomness of the source and apply a randomness
extractor. Here, we propose a generic framework for evaluating quantum
randomness of real-life QRNGs by min-entropy, and apply it to two different
existing quantum random-number systems in the literature. Moreover, we provide
a guideline of QRNG data postprocessing for which we implement two
information-theoretically provable randomness extractors: Toeplitz-hashing
extractor and Trevisan's extractor.Comment: 13 pages, 2 figure
Experimental study of quantum random number generator based on two independent lasers
Quantum random number generator (QRNG) can produce true randomness by
utilizing the inherent probabilistic nature of quantum mechanics. Recently, the
spontaneous-emission quantum phase noise of the laser has been widely deployed
for QRNG, due to its high rate, low cost and the feasibility of chip-scale
integration. Here, we perform a comprehensive experimental study of phase-noise
based QRNG with two independent lasers, each of which operates in either
continuous-wave (CW) or pulsed mode. We implement QRNGs by operating the two
lasers in three configurations, namely CW+CW, CW+pulsed and pulsed+pulsed, and
demonstrate their tradeoffs, strengths and weaknesses.Comment: 7pages,6figures.It has been accepted by PR
Unbiased All-Optical Random-Number Generator
The generation of random bits is of enormous importance in modern information
science. Cryptographic security is based on random numbers which require a
physical process for their generation. This is commonly performed by hardware
random number generators. These exhibit often a number of problems, namely
experimental bias, memory in the system, and other technical subtleties, which
reduce the reliability in the entropy estimation. Further, the generated
outcome has to be post-processed to "iron out" such spurious effects. Here, we
present a purely optical randomness generator, based on the bi-stable output of
an optical parametric oscillator. Detector noise plays no role and no further
post-processing is required. Upon entering the bi-stable regime, initially the
resulting output phase depends on vacuum fluctuations. Later, the phase is
rigidly locked and can be well determined versus a pulse train, which is
derived from the pump laser. This delivers an ambiguity-free output, which is
reliably detected and associated with a binary outcome. The resulting random
bit stream resembles a perfect coin toss and passes all relevant randomness
measures. The random nature of the generated binary outcome is furthermore
confirmed by an analysis of resulting conditional entropies.Comment: 10 pages, 4 figure
ANCHOR: logically-centralized security for Software-Defined Networks
While the centralization of SDN brought advantages such as a faster pace of
innovation, it also disrupted some of the natural defenses of traditional
architectures against different threats. The literature on SDN has mostly been
concerned with the functional side, despite some specific works concerning
non-functional properties like 'security' or 'dependability'. Though addressing
the latter in an ad-hoc, piecemeal way, may work, it will most likely lead to
efficiency and effectiveness problems. We claim that the enforcement of
non-functional properties as a pillar of SDN robustness calls for a systemic
approach. As a general concept, we propose ANCHOR, a subsystem architecture
that promotes the logical centralization of non-functional properties. To show
the effectiveness of the concept, we focus on 'security' in this paper: we
identify the current security gaps in SDNs and we populate the architecture
middleware with the appropriate security mechanisms, in a global and consistent
manner. Essential security mechanisms provided by anchor include reliable
entropy and resilient pseudo-random generators, and protocols for secure
registration and association of SDN devices. We claim and justify in the paper
that centralizing such mechanisms is key for their effectiveness, by allowing
us to: define and enforce global policies for those properties; reduce the
complexity of controllers and forwarding devices; ensure higher levels of
robustness for critical services; foster interoperability of the non-functional
property enforcement mechanisms; and promote the security and resilience of the
architecture itself. We discuss design and implementation aspects, and we prove
and evaluate our algorithms and mechanisms, including the formalisation of the
main protocols and the verification of their core security properties using the
Tamarin prover.Comment: 42 pages, 4 figures, 3 tables, 5 algorithms, 139 reference
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