3,391 research outputs found
Quantitative information flow under generic leakage functions and adaptive adversaries
We put forward a model of action-based randomization mechanisms to analyse
quantitative information flow (QIF) under generic leakage functions, and under
possibly adaptive adversaries. This model subsumes many of the QIF models
proposed so far. Our main contributions include the following: (1) we identify
mild general conditions on the leakage function under which it is possible to
derive general and significant results on adaptive QIF; (2) we contrast the
efficiency of adaptive and non-adaptive strategies, showing that the latter are
as efficient as the former in terms of length up to an expansion factor bounded
by the number of available actions; (3) we show that the maximum information
leakage over strategies, given a finite time horizon, can be expressed in terms
of a Bellman equation. This can be used to compute an optimal finite strategy
recursively, by resorting to standard methods like backward induction.Comment: Revised and extended version of conference paper with the same title
appeared in Proc. of FORTE 2014, LNC
X-Ray Detection of Transient Magnetic Moments Induced by a Spin Current in Cu
We have used a MHz lock-in x-ray spectro-microscopy technique to directly
detect changes of magnetic moments in Cu due to spin injection from an adjacent
Co layer. The elemental and chemical specificity of x-rays allows us to
distinguish two spin current induced effects. We detect the creation of
transient magnetic moments of on Cu atoms
within the bulk of the 28 nm thick Cu film due to spin-accumulation. The moment
value is compared to predictions by Mott's two current model. We also observe
that the hybridization induced existing magnetic moments on Cu interface atoms
are transiently increased by about 10% or .
This reveals the dominance of spin-torque alignment over Joule heat induced
disorder of the interfacial Cu moments during current flow
Updates-Leak: Data Set Inference and Reconstruction Attacks in Online Learning
Machine learning (ML) has progressed rapidly during the past decade and the major factor that drives such development is the unprecedented large-scale data. As data generation is a continuous process, this leads to ML service providers updating their models frequently with newly-collected data in an online learning scenario. In consequence, if an ML model is queried with the same set of data samples at two different points in time, it will provide different results. In this paper, we investigate whether the change in the output of a black-box ML model before and after being updated can leak information of the dataset used to perform the update. This constitutes a new attack surface against black-box ML models and such information leakage severely damages the intellectual property and data privacy of the ML model owner/provider. In contrast to membership inference attacks, we use an encoder-decoder formulation that allows inferring diverse information ranging from detailed characteristics to full reconstruction of the dataset. Our new attacks are facilitated by state-of-the-art deep learning techniques. In particular, we propose a hybrid generative model (BM-GAN) that is based on generative adversarial networks (GANs) but includes a reconstructive loss that allows generating accurate samples. Our experiments show effective prediction of dataset characteristics and even full reconstruction in challenging conditions
Towards Inferring Mechanical Lock Combinations using Wrist-Wearables as a Side-Channel
Wrist-wearables such as smartwatches and fitness bands are equipped with a
variety of high-precision sensors that support novel contextual and
activity-based applications. The presence of a diverse set of on-board sensors,
however, also expose an additional attack surface which, if not adequately
protected, could be potentially exploited to leak private user information. In
this paper, we investigate the feasibility of a new attack that takes advantage
of a wrist-wearable's motion sensors to infer input on mechanical devices
typically used to secure physical access, for example, combination locks. We
outline an inference framework that attempts to infer a lock's unlock
combination from the wrist motion captured by a smartwatch's gyroscope sensor,
and uses a probabilistic model to produce a ranked list of likely unlock
combinations. We conduct a thorough empirical evaluation of the proposed
framework by employing unlocking-related motion data collected from human
subject participants in a variety of controlled and realistic settings.
Evaluation results from these experiments demonstrate that motion data from
wrist-wearables can be effectively employed as a side-channel to significantly
reduce the unlock combination search-space of commonly found combination locks,
thus compromising the physical security provided by these locks
Computation of significance scores of unweighted Gene Set Enrichment Analyses
<p>Abstract</p> <p>Background</p> <p>Gene Set Enrichment Analysis (GSEA) is a computational method for the statistical evaluation of sorted lists of genes or proteins. Originally GSEA was developed for interpreting microarray gene expression data, but it can be applied to any sorted list of genes. Given the gene list and an arbitrary biological category, GSEA evaluates whether the genes of the considered category are randomly distributed or accumulated on top or bottom of the list. Usually, significance scores (p-values) of GSEA are computed by nonparametric permutation tests, a time consuming procedure that yields only estimates of the p-values.</p> <p>Results</p> <p>We present a novel dynamic programming algorithm for calculating exact significance values of unweighted Gene Set Enrichment Analyses. Our algorithm avoids typical problems of nonparametric permutation tests, as varying findings in different runs caused by the random sampling procedure. Another advantage of the presented dynamic programming algorithm is its runtime and memory efficiency. To test our algorithm, we applied it not only to simulated data sets, but additionally evaluated expression profiles of squamous cell lung cancer tissue and autologous unaffected tissue.</p
Deep Learning and Random Forest-Based Augmentation of sRNA Expression Profiles
The lack of well-structured annotations in a growing amount of RNA expression
data complicates data interoperability and reusability. Commonly - used text
mining methods extract annotations from existing unstructured data descriptions
and often provide inaccurate output that requires manual curation. Automatic
data-based augmentation (generation of annotations on the base of expression
data) can considerably improve the annotation quality and has not been
well-studied. We formulate an automatic augmentation of small RNA-seq
expression data as a classification problem and investigate deep learning (DL)
and random forest (RF) approaches to solve it. We generate tissue and sex
annotations from small RNA-seq expression data for tissues and cell lines of
homo sapiens. We validate our approach on 4243 annotated small RNA-seq samples
from the Small RNA Expression Atlas (SEA) database. The average prediction
accuracy for tissue groups is 98% (DL), for tissues - 96.5% (DL), and for sex -
77% (DL). The "one dataset out" average accuracy for tissue group prediction is
83% (DL) and 59% (RF). On average, DL provides better results as compared to
RF, and considerably improves classification performance for 'unseen' datasets
Skill obsolescence, vintage effects and changing tasks
Human capital is no doubt one of the most important factors for future economic growth and well-being. However, human capital is also prone to becoming obsolete over time. Skills that have been acquired at one point in time may perfectly match the skill requirements at that time but may become obsolete as time goes by. Thus, in the following paper, we study the depreciation processes of the human capital of workers performing different types of tasks with different skill requirements over a period of more than twenty years. We argue that two types of tasks must be distinguished: knowledge-based tasks and experience-based tasks. Knowledge-based tasks demand skills depending on the actual stock of technological knowledge in a society whereas experience-based tasks demand skills depending on personal factors and individual experience values. We show, by applying Mincer regressions on four different cross sections, that the human capital of people performing knowledge-based tasks suffers more from depreciation than the human capital of individuals performing experience-based tasks
On the Gold Standard for Security of Universal Steganography
While symmetric-key steganography is quite well understood both in the
information-theoretic and in the computational setting, many fundamental
questions about its public-key counterpart resist persistent attempts to solve
them. The computational model for public-key steganography was proposed by von
Ahn and Hopper in EUROCRYPT 2004. At TCC 2005, Backes and Cachin gave the first
universal public-key stegosystem - i.e. one that works on all channels -
achieving security against replayable chosen-covertext attacks (SS-RCCA) and
asked whether security against non-replayable chosen-covertext attacks (SS-CCA)
is achievable. Later, Hopper (ICALP 2005) provided such a stegosystem for every
efficiently sampleable channel, but did not achieve universality. He posed the
question whether universality and SS-CCA-security can be achieved
simultaneously. No progress on this question has been achieved since more than
a decade. In our work we solve Hopper's problem in a somehow complete manner:
As our main positive result we design an SS-CCA-secure stegosystem that works
for every memoryless channel. On the other hand, we prove that this result is
the best possible in the context of universal steganography. We provide a
family of 0-memoryless channels - where the already sent documents have only
marginal influence on the current distribution - and prove that no
SS-CCA-secure steganography for this family exists in the standard
non-look-ahead model.Comment: EUROCRYPT 2018, llncs styl
Hubbard band or oxygen vacancy states in the correlated electron metal SrVO?
We study the effect of oxygen vacancies on the electronic structure of the
model strongly correlated metal SrVO. By means of angle-resolved
photoemission (ARPES) synchrotron experiments, we investigate the systematic
effect of the UV dose on the measured spectra. We observe the onset of a
spurious dose-dependent prominent peak at an energy range were the lower
Hubbard band has been previously reported in this compound, raising questions
on its previous interpretation. By a careful analysis of the dose dependent
effects we succeed in disentangling the contributions coming from the oxygen
vacancy states and from the lower Hubbard band. We obtain the intrinsic ARPES
spectrum for the zero-vacancy limit, where a clear signal of a lower Hubbard
band remains. We support our study by means of state-of-the-art ab initio
calculations that include correlation effects and the presence of oxygen
vacancies. Our results underscore the relevance of potential spurious states
affecting ARPES experiments in correlated metals, which are associated to the
ubiquitous oxygen vacancies as extensively reported in the context of a
two-dimensional electron gas (2DEG) at the surface of insulating
transition metal oxides.Comment: Manuscript + Supplemental Material, 12 pages, 9 figure
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