Advanced Cloud Privacy Threat Modeling

Privacy-preservation for sensitive data has become a challenging issue in cloud computing. Threat modeling as a part of requirements engineering in secure software development provides a structured approach for identifying attacks and proposing countermeasures against the exploitation of vulnerabilities in a system . This paper describes an extension of Cloud Privacy Threat Modeling (CPTM) methodology for privacy threat modeling in relation to processing sensitive data in cloud computing environments. It describes the modeling methodology that involved applying Method Engineering to specify characteristics of a cloud privacy threat modeling methodology, different steps in the proposed methodology and corresponding products. We believe that the extended methodology facilitates the application of a privacy-preserving cloud software development approach from requirements engineering to design

The effect of surface heterogeneity on cloud absorption estimates

This study presents a systematic and quantitative analysis of the effect of inhomogeneous surface albedo on shortwave cloud absorption estimates. We used 3D radiative transfer modeling over a checkerboard surface albedo to calculate cloud absorption. We have found that accounting for surface heterogeneity enhances cloud absorption. However, the enhancement is not sufficient to explain the reported difference between measured and modeled cloud absorption

A modeling analysis program for the JPL Table Mountain Io sodium cloud data

The abundant Io sodium cloud data obtained at JPL Table Mountain was reviewed. Images of the sodium cloud important to this modeling analysis program are contained in the 1976-1979 data set and the 1981 data set. A preliminary assessment of the 263 images in the 1981 data set for Region B/C was initiated. The spatial morphology of some of these images revealed the presence of the forward sodium cloud (Region B) and the directional features (Region C) as expected. Plans for the second quarter to initiate preliminary modeling analysis and to define further data processing are discussed

Maximum likelihood estimation of cloud height from multi-angle satellite imagery

We develop a new estimation technique for recovering depth-of-field from multiple stereo images. Depth-of-field is estimated by determining the shift in image location resulting from different camera viewpoints. When this shift is not divisible by pixel width, the multiple stereo images can be combined to form a super-resolution image. By modeling this super-resolution image as a realization of a random field, one can view the recovery of depth as a likelihood estimation problem. We apply these modeling techniques to the recovery of cloud height from multiple viewing angles provided by the MISR instrument on the Terra Satellite. Our efforts are focused on a two layer cloud ensemble where both layers are relatively planar, the bottom layer is optically thick and textured, and the top layer is optically thin. Our results demonstrate that with relative ease, we get comparable estimates to the M2 stereo matcher which is the same algorithm used in the current MISR standard product (details can be found in [IEEE Transactions on Geoscience and Remote Sensing 40 (2002) 1547--1559]). Moreover, our techniques provide the possibility of modeling all of the MISR data in a unified way for cloud height estimation. Research is underway to extend this framework for fast, quality global estimates of cloud height.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS243 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Self-organizing nonlinear output (SONO): A neural network suitable for cloud patch-based rainfall estimation at small scales

Accurate measurement of rainfall distribution at various spatial and temporal scales is crucial for hydrological modeling and water resources management. In the literature of satellite rainfall estimation, many efforts have been made to calibrate a statistical relationship (including threshold, linear, or nonlinear) between cloud infrared (IR) brightness temperatures and surface rain rates (RR). In this study, an automated neural network for cloud patch-based rainfall estimation, entitled self-organizing nonlinear output (SONO) model, is developed to account for the high variability of cloud-rainfall processes at geostationary scales (i.e., 4 km and every 30 min). Instead of calibrating only one IR-RR function for all clouds the SONO classifies varied cloud patches into different clusters and then searches a nonlinear IR-RR mapping function for each cluster. This designed feature enables SONO to generate various rain rates at a given brightness temperature and variable rain/no-rain IR thresholds for different cloud types, which overcomes the one-to-one mapping limitation of a single statistical IR-RR function for the full spectrum of cloud-rainfall conditions. In addition, the computational and modeling strengths of neural network enable SONO to cope with the nonlinearity of cloud-rainfall relationships by fusing multisource data sets. Evaluated at various temporal and spatial scales, SONO shows improvements of estimation accuracy, both in rain intensity and in detection of rain/no-rain pixels. Further examination of the SONO adaptability demonstrates its potentiality as an operational satellite rainfall estimation system that uses the passive microwave rainfall observations from low-orbiting satellites to adjust the IR-based rainfall estimates at the resolution of geostationary satellites. Copyright 2005 by the American Geophysical Union

Modeling the Formation of Clouds in Brown Dwarf Atmospheres

Because the opacity of clouds in substellar mass object (SMO) atmospheres depends on the composition and distribution of particle sizes within the cloud, a credible cloud model is essential for accurately modeling SMO spectra and colors. We present a one--dimensional model of cloud particle formation and subsequent growth based on a consideration of basic cloud microphysics. We apply this microphysical cloud model to a set of synthetic brown dwarf atmospheres spanning a broad range of surface gravities and effective temperatures (g_surf = 1.78 * 10^3 -- 3 * 10^5 cm/s^2 and T_eff = 600 -- 1600 K) to obtain plausible particle sizes for several abundant species (Fe, Mg2SiO4, and Ca2Al2SiO7). At the base of the clouds, where the particles are largest, the particle sizes thus computed range from ~5 microns to over 300 microns in radius over the full range of atmospheric conditions considered. We show that average particle sizes decrease significantly with increasing brown dwarf surface gravity. We also find that brown dwarfs with higher effective temperatures have characteristically larger cloud particles than those with lower effective temperatures. We therefore conclude that it is unrealistic when modeling SMO spectra to apply a single particle size distribution to the entire class of objects.Comment: 25 pages; 8 figures. We have added considerable detail describing the physics of the cloud model. We have also added discussions of the issues of rainout and the self-consistent coupling of clouds with brown dwarf atmospheric models. We have updated figures 1, 3, and 4 with new vertical axis labels and new particle sizes for forsterite and gehlenite. Accepted to the Astrophysical Journal, Dec. 2, 200

A Factor Framework for Experimental Design for Performance Evaluation of Commercial Cloud Services

Given the diversity of commercial Cloud services, performance evaluations of candidate services would be crucial and beneficial for both service customers (e.g. cost-benefit analysis) and providers (e.g. direction of service improvement). Before an evaluation implementation, the selection of suitable factors (also called parameters or variables) plays a prerequisite role in designing evaluation experiments. However, there seems a lack of systematic approaches to factor selection for Cloud services performance evaluation. In other words, evaluators randomly and intuitively concerned experimental factors in most of the existing evaluation studies. Based on our previous taxonomy and modeling work, this paper proposes a factor framework for experimental design for performance evaluation of commercial Cloud services. This framework capsules the state-of-the-practice of performance evaluation factors that people currently take into account in the Cloud Computing domain, and in turn can help facilitate designing new experiments for evaluating Cloud services.Comment: 8 pages, Proceedings of the 4th International Conference on Cloud Computing Technology and Science (CloudCom 2012), pp. 169-176, Taipei, Taiwan, December 03-06, 201

A comparison of chemistry and dust cloud formation in ultracool dwarf model atmospheres

The atmospheres of substellar objects contain clouds of oxides, iron, silicates, and other refractory condensates. Water clouds are expected in the coolest objects. The opacity of these `dust' clouds strongly affects both the atmospheric temperature-pressure profile and the emergent flux. Thus any attempt to model the spectra of these atmospheres must incorporate a cloud model. However the diversity of cloud models in atmospheric simulations is large and it is not always clear how the underlying physics of the various models compare. Likewise the observational consequences of different modeling approaches can be masked by other model differences, making objective comparisons challenging. In order to clarify the current state of the modeling approaches, this paper compares five different cloud models in two sets of tests. Test case 1 tests the dust cloud models for a prescribed L, L--T, and T-dwarf atmospheric (temperature T, pressure p, convective velocity vconv)-structures. Test case 2 compares complete model atmosphere results for given (effective temperature Teff, surface gravity log g). All models agree on the global cloud structure but differ in opacity-relevant details like grain size, amount of dust, dust and gas-phase composition. Comparisons of synthetic photometric fluxes translate into an modelling uncertainty in apparent magnitudes for our L-dwarf (T-dwarf) test case of 0.25 < \Delta m < 0.875 (0.1 < \Delta m M 1.375) taking into account the 2MASS, the UKIRT WFCAM, the Spitzer IRAC, and VLT VISIR filters with UKIRT WFCAM being the most challenging for the models. (abr.)Comment: 22 pages, 17 figures, MNRAS 2008, accepted, (minor grammar/typo corrections