Quantifying Information Leaks Using Reliability Analysis

acmid: 2632367 keywords: Model Counting, Quantitative Information Flow, Reliability Analysis, Symbolic Execution location: San Jose, CA, USA numpages: 4acmid: 2632367 keywords: Model Counting, Quantitative Information Flow, Reliability Analysis, Symbolic Execution location: San Jose, CA, USA numpages: 4acmid: 2632367 keywords: Model Counting, Quantitative Information Flow, Reliability Analysis, Symbolic Execution location: San Jose, CA, USA numpages: 4We report on our work-in-progress into the use of reliability analysis to quantify information leaks. In recent work we have proposed a software reliability analysis technique that uses symbolic execution and model counting to quantify the probability of reaching designated program states, e.g. assert violations, under uncertainty conditions in the environment. The technique has many applications beyond reliability analysis, ranging from program understanding and debugging to analysis of cyber-physical systems. In this paper we report on a novel application of the technique, namely Quantitative Information Flow analysis (QIF). The goal of QIF is to measure information leakage of a program by using information-theoretic metrics such as Shannon entropy or Renyi entropy. We exploit the model counting engine of the reliability analyzer over symbolic program paths, to compute an upper bound of the maximum leakage over all possible distributions of the confidential data. We have implemented our approach into a prototype tool, called QILURA, and explore its effectiveness on a number of case studie

Statistical quality assessment and outlier detection for liquid chromatography-mass spectrometry experiments

<p>Abstract</p> <p>Background</p> <p>Quality assessment methods, that are common place in engineering and industrial production, are not widely spread in large-scale proteomics experiments. But modern technologies such as Multi-Dimensional Liquid Chromatography coupled to Mass Spectrometry (LC-MS) produce large quantities of proteomic data. These data are prone to measurement errors and reproducibility problems such that an automatic quality assessment and control become increasingly important.</p> <p>Results</p> <p>We propose a methodology to assess the quality and reproducibility of data generated in quantitative LC-MS experiments. We introduce quality descriptors that capture different aspects of the quality and reproducibility of LC-MS data sets. Our method is based on the Mahalanobis distance and a robust Principal Component Analysis.</p> <p>Conclusion</p> <p>We evaluate our approach on several data sets of different complexities and show that we are able to precisely detect LC-MS runs of poor signal quality in large-scale studies.</p

Surface plasmon resonance imaging of cells and surface-associated fibronectin

<p>Abstract</p> <p>Background</p> <p>A critical challenge in cell biology is quantifying the interactions of cells with their extracellular matrix (ECM) environment and the active remodeling by cells of their ECM. Fluorescence microscopy is a commonly employed technique for examining cell-matrix interactions. A label-free imaging method would provide an alternative that would eliminate the requirement of transfected cells and modified biological molecules, and if collected nondestructively, would allow long term observation and analysis of live cells.</p> <p>Results</p> <p>Using surface plasmon resonance imaging (SPRI), the deposition of protein by vascular smooth muscle cells (vSMC) cultured on fibronectin was quantified as a function of cell density and distance from the cell periphery. We observed that as much as 120 ng/cm²of protein was deposited by cells in 24 h.</p> <p>Conclusion</p> <p>SPRI is a real-time, low-light-level, label-free imaging technique that allows the simultaneous observation and quantification of protein layers and cellular features. This technique is compatible with live cells such that it is possible to monitor cellular modifications to the extracellular matrix in real-time.</p

A novel method for quantified, superresolved, three-dimensional colocalisation of isotropic, fluorescent particles

Colocalisation, the overlap of subcellular structures labelled with different colours, is a key step to characterise cellular phenotypes. We have developed a novel bioimage informatics approach for quantifying colocalisation of round, blob-like structures in two-colour, highly resolved, three-dimensional fluorescence microscopy datasets. First, the algorithm identifies isotropic fluorescent particles, of relative brightness compared to their immediate neighbourhood, in three dimensions and for each colour. The centroids of these spots are then determined, and each object in one location of a colour image is checked for a corresponding object in the other colour image. Three-dimensional distance maps between the centroids of differently coloured spots then display where and how closely they colocalise, while histograms allow to analyse all colocalisation distances. We use the method to reveal sparse colocalisation of different human leukocyte antigen receptors in choriocarcinoma cells. It can also be applied to other isotropic subcellular structures such as vesicles, aggresomes and chloroplasts. The simple, robust and fast approach yields superresolved, object-based colocalisation maps and provides a first indication of protein–protein interactions of fluorescent, isotropic particles