2,612 research outputs found
Die Marktbearbeitung in der Automobilzulieferindustrie : Strategien, Erfolgsfaktoren und Fallstricke
Die sich verÀndernden Marktgegebenheiten in
der Automobilindustrie stellen das
Management von Automobilzulieferunternehmen vor immer neue Herausforderungen. Durch neue Einkaufs- bzw. Beschaffungskonzepte der Automobilhersteller, wie z.B. Outsourcing, Global Sourcing, Single Sourcing und Modular/System Sourcing,
verÀndert sich das Gesicht der Automobilzulieferindustrie mit hoher Drehzahl.
Das Institut fĂŒr Marktorientierte UnternehmensfĂŒhrung (IMU) der UniversitĂ€t Mannheim und die Unternehmensberatung Prof.
Homburg & Partner haben im Rahmen
einer Analyse der gesamten
Automobilzulieferindustrie
zum Thema âMarktbearbeitungâ eine Vollerhebung bei insgesamt 1.024 GeschĂ€ftsbereichen der gröĂten 313 internationalen Automobilzulieferern
aller Wertschöpfungsstufen durchgefĂŒhrt.
Die Bestandsaufnahme liefert interessante Ergebnisse: Beispielsweise gelingt es
erfolgreichen Automobilzulieferern bei der
Marktbearbeitung wesentlich besser, sich
zu fokussieren. Entscheidend dabei ist eine engere Produktpalette, die international
angeboten wird. Dies gelingt mittelstÀndischen Automobilzulieferern meist besser als
sogenannten âMega-Suppliernâ. AuĂerdem ist, aufgrund der hohen Bedeutung der QualitĂ€t, zwar fast jeder der untersuchten Automobilzulieferer zertifiziert, aber gleichzeitig werden nicht alle Instrumente des QualitĂ€tsmanagements
systematisch eingesetzt.
Neben der ausfĂŒhrlichen Darstellung der
Bestandsaufnahme der Marktbearbeitung
werden Erfolgsfaktoren aus insgesamt
sechs verschiedenen Bereichen der Marktbearbeitung vorgestellt
Likelihood Non-Gaussianity in Large-Scale Structure Analyses
Standard present day large-scale structure (LSS) analyses make a major
assumption in their Bayesian parameter inference --- that the likelihood has a
Gaussian form. For summary statistics currently used in LSS, this assumption,
even if the underlying density field is Gaussian, cannot be correct in detail.
We investigate the impact of this assumption on two recent LSS analyses: the
Beutler et al. (2017) power spectrum multipole () analysis and the
Sinha et al. (2017) group multiplicity function () analysis. Using
non-parametric divergence estimators on mock catalogs originally constructed
for covariance matrix estimation, we identify significant non-Gaussianity in
both the and likelihoods. We then use Gaussian mixture density
estimation and Independent Component Analysis on the same mocks to construct
likelihood estimates that approximate the true likelihood better than the
Gaussian -likelihood. Using these likelihood estimates, we accurately
estimate the true posterior probability distribution of the Beutler et al.
(2017) and Sinha et al. (2017) parameters. Likelihood non-Gaussianity shifts
the constraint by , but otherwise, does not
significantly impact the overall parameter constraints of Beutler et al.
(2017). For the analysis, using the pseudo-likelihood significantly
underestimates the uncertainties and biases the constraints of Sinha et al.
(2017) halo occupation parameters. For and , the posteriors
are shifted by and and broadened by and
, respectively. The divergence and likelihood estimation methods we
present provide a straightforward framework for quantifying the impact of
likelihood non-Gaussianity and deriving more accurate parameter constraints.Comment: 33 pages, 7 figure
A Highly Accurate Query-Recovery Attack against Searchable Encryption using Non-Indexed Documents
Cloud data storage solutions offer customers cost-effective and reduced data
management. While attractive, data security issues remain to be a core concern.
Traditional encryption protects stored documents, but hinders simple
functionalities such as keyword search. Therefore, searchable encryption
schemes have been proposed to allow for the search on encrypted data. Efficient
schemes leak at least the access pattern (the accessed documents per keyword
search), which is known to be exploitable in query recovery attacks assuming
the attacker has a significant amount of background knowledge on the stored
documents. Existing attacks can only achieve decent results with strong
adversary models (e.g. at least 20% of previously known documents or require
additional knowledge such as on query frequencies) and they give no metric to
evaluate the certainty of recovered queries. This hampers their practical
utility and questions their relevance in the real-world.
We propose a refined score attack which achieves query recovery rates of
around 85% without requiring exact background knowledge on stored documents; a
distributionally similar, but otherwise different (i.e., non-indexed), dataset
suffices. The attack starts with very few known queries (around 10 known
queries in our experiments over different datasets of varying size) and then
iteratively recovers further queries with confidence scores by adding
previously recovered queries that had high confidence scores to the set of
known queries. Additional to high recovery rates, our approach yields
interpretable results in terms of confidence scores.Comment: Published in USENIX 2021. Full version with extended appendices and
removed some typo
Fluid-limit Cosmological Simulations Starting from the Big Bang
The cosmic large-scale structure (LSS) provides a unique testing ground for
connecting fundamental physics to astronomical observations. Modelling the LSS
requires numerical -body simulations or perturbative techniques that both
come with distinct shortcomings. Here we present the first unified numerical
approach, enabled by new time integration and discreteness reduction schemes,
and demonstrate its convergence at the field level. In particular, we show that
our simulations (1) can be initialised directly at time zero, and (2) can be
made to agree with high-order Lagrangian perturbation theory in the fluid
limit. This allows fast, self-consistent, and UV-complete forward modelling of
LSS observables.Comment: 5+10 pages, 4+7 figures. Comments are very welcome
Experimental Review of the IKK Query Recovery Attack:Assumptions, Recovery Rate and Improvements
Practical yet Provably Secure: Complex Database Query Execution over Encrypted Data
Encrypted databases provide security for outsourced data. In this work novel encryption schemes supporting different database query types are presented enabling complex database queries over encrypted data. For specific constructions enabling exact keyword queries, range queries, database joins and substring queries over encrypted data we prove security in a formal framework, present a theoretical runtime analysis and provide an assessment of practical performance characteristics
Improved Multiplication-Free Biometric Recognition under Encryption
Modern biometric recognition systems extract distinctive feature vectors of biometric samples using deep neural networks to measure the amount of (dis-)similarity between two biometric samples. Studies have shown that personal information (e.g., health condition, ethnicity, etc.) can be inferred, and biometric samples can be reconstructed from those feature vectors, making their protection an urgent necessity. State-of-the-art biometrics protection solutions are based on homomorphic encryption (HE) to perform recognition over encrypted feature vectors, hiding the features and their processing while releasing the outcome only. However, this comes at the cost of those solutions' efficiency due to the inefficiency of HE-based solutions with a large number of multiplications; for (dis-)similarity measures, this number is proportional to the vector's dimension.In this paper, we tackle the HE performance bottleneck by freeing the two common (dis-)similarity measures, the cosine similarity and the squared Euclidean distance, from multiplications. Assuming normalized feature vectors, our approach pre-computes and organizes those (dis-)similarity measures into lookup tables. This transforms their computation into simple table lookups and summations only. We integrate the table lookup with HE and introduce pseudo-random permutations to enable cheap plaintext slot selection, which significantly saves the recognition runtime and brings a positive impact on the recognition performance. We then assess their runtime efficiency under encryption and record runtimes between 16.74ms and 49.84ms for both the cleartext and encrypted decision modes over the three security levels, demonstrating their enhanced speed for a compact encrypted reference template reduced to one ciphertext
Hybrid biometric template protection:Resolving the agony of choice between bloom filters and homomorphic encryption
Abstract Bloom filters (BFs) and homomorphic encryption (HE) are prominent techniques used to design biometric template protection (BTP) schemes that aim to protect sensitive biometric information during storage and biometric comparison. However, the pros and cons of BFâ and HEâbased BTPs are not well studied in literature. We investigate the strengths and weaknesses of these two approaches since both seem promising from a theoretical viewpoint. Our key insight is to extend our theoretical investigation to cover the practical case of iris recognition on the ground that iris (1) benefits from the alignmentâfree property of BFs and (2) induces huge computational burdens when implemented in the HEâencrypted domain. BFâbased BTPs can be implemented to be either fast with high recognition accuracy while missing the important privacy property of âunlinkabilityâ, or to be fast with unlinkabilityâproperty while missing the high accuracy. HEâbased BTPs, on the other hand, are highly secure, achieve good accuracy, and meet the unlinkabilityâproperty, but they are much slower than BFâbased approaches. As a synthesis, we propose a hybrid BTP scheme that combines the good properties of BFs and HE, ensuring unlinkability and high recognition accuracy, while being about seven times faster than the traditional HEâbased approach
Electroweak corrections to production at hadron colliders
In this paper we present the results from a calculation of the full
electroweak one-loop corrections for vector-boson pair production at
hadron colliders. The cases of proton--antiproton as well as proton--proton
collisions, at the Tevatron and the LHC, respectively, are considered. Results
are presented for the distribution of the invariant mass and for the
transverse momentum of the final-state photon. The higher-order electroweak
effects are numerically significant, in particular for probing possible
anomalous gauge-boson couplings
I Still Know What You Watched Last Sunday: Privacy of the HbbTV Protocol in the European Smart TV Landscape
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