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 $3\times 10^{-5}$ $\mu_\mathrm{B}$ 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 $4\times 10^{-3}$ $\mu_\mathrm{B}$. 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 SrVO3_3?

We study the effect of oxygen vacancies on the electronic structure of the model strongly correlated metal SrVO$_3$. 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 $d^0$ transition metal oxides.Comment: Manuscript + Supplemental Material, 12 pages, 9 figure