k-Nearest Neighbor Classification over Semantically Secure Encrypted Relational Data

Data Mining has wide applications in many areas such as banking, medicine, scientific research and among government agencies. Classification is one of the commonly used tasks in data mining applications. For the past decade, due to the rise of various privacy issues, many theoretical and practical solutions to the classification problem have been proposed under different security models. However, with the recent popularity of cloud computing, users now have the opportunity to outsource their data, in encrypted form, as well as the data mining tasks to the cloud. Since the data on the cloud is in encrypted form, existing privacy preserving classification techniques are not applicable. In this paper, we focus on solving the classification problem over encrypted data. In particular, we propose a secure k-NN classifier over encrypted data in the cloud. The proposed k-NN protocol protects the confidentiality of the data, user's input query, and data access patterns. To the best of our knowledge, our work is the first to develop a secure k-NN classifier over encrypted data under the semi-honest model. Also, we empirically analyze the efficiency of our solution through various experiments.Comment: 29 pages, 2 figures, 3 tables arXiv admin note: substantial text overlap with arXiv:1307.482

Secure k-Nearest Neighbor Query over Encrypted Data in Outsourced Environments

For the past decade, query processing on relational data has been studied extensively, and many theoretical and practical solutions to query processing have been proposed under various scenarios. With the recent popularity of cloud computing, users now have the opportunity to outsource their data as well as the data management tasks to the cloud. However, due to the rise of various privacy issues, sensitive data (e.g., medical records) need to be encrypted before outsourcing to the cloud. In addition, query processing tasks should be handled by the cloud; otherwise, there would be no point to outsource the data at the first place. To process queries over encrypted data without the cloud ever decrypting the data is a very challenging task. In this paper, we focus on solving the k-nearest neighbor (kNN) query problem over encrypted database outsourced to a cloud: a user issues an encrypted query record to the cloud, and the cloud returns the k closest records to the user. We first present a basic scheme and demonstrate that such a naive solution is not secure. To provide better security, we propose a secure kNN protocol that protects the confidentiality of the data, user's input query, and data access patterns. Also, we empirically analyze the efficiency of our protocols through various experiments. These results indicate that our secure protocol is very efficient on the user end, and this lightweight scheme allows a user to use any mobile device to perform the kNN query.Comment: 23 pages, 8 figures, and 4 table

Radiation Damage in Polarized Ammonia Solids

Solid NH3 and ND3 provide a highly polarizable, radiation resistant source of polarized protons and deuterons and have been used extensively in high luminosity experiments investigating the spin structure of the nucleon. Over the past twenty years, the UVA polarized target group has been instrumental in producing and polarizing much of the material used in these studies, and many practical considerations have been learned in this time. In this discussion, we analyze the polarization performance of the solid ammonia targets used during the recent JLab Eg4 run. Topics include the rate of polarization decay with accumulated charge, the annealing procedure for radiation damaged targets to recover polarization, and the radiation induced change in optimum microwave frequency used to polarize the sample. We also discuss the success we have had in implementing frequency modulation of the polarizing microwave frequency.Comment: 5 pages, 6 figures. XIIth International Workshop on Polarized Sources, Targets and Polarimetr

Public infrastructure and private investment in the Middle East and North Africa

The authors examine the impact of public infrastructure on private capital formation in three countries of the Middle East and North Africa-Egypt, Jordan, and Tunisia. They highlight various channels through which public infrastructure may affect private investment. Then they describe their empirical framework, which is based on a vector autoregression (VAR) model that accounts for flows and (quality-adjusted) stocks of public infrastructure, private investment, as well as changes in output, private sector credit, and the real exchange rate. The authors propose two aggregate measures of the quality of public infrastructure and use principal components to derive a composite indicator. Their analysis suggests that public infrastructure has both"flow"and"stock"effects on private investment in Egypt, but only a"stock"effect in Jordan and Tunisia. But these effects are small and short-lived, reflecting the unfavorable environment for private investment in their sample of countries. Reducing unproductive public capital expenditure and improving quality must be accompanied by policy reforms aimed at limiting investment to infrastructure capital that crowds in the private sector and corrects for fundamental market failures. This will entail privatization and greater involvement of the private sector in infrastructure investment. While infrastructure (in the form of the provision of critical telecommunications, transport, and energy services) is important, other improvements in the environment in which domestic investment is conducted are crucial. These include the need to provide financing on adequate terms and guarantee a secure and efficient justice system.

Labor market reforms, growth, and unemployment in labor-exporting countries in the Middle East and North Africa

This paper studies the impact of labor market policies on growth and unemployment in labor-exporting countries in the Middle East and North Africa. The analysis is based on a framework that captures many of the main features of the labor market in these countries. We conduct a variety of policy experiments, including a reduction in payroll taxation, cuts in public sector wages and employment, an increase in employment subsidies, a reduction in trade unions'bargaining power, and a composite reform program. Our key message is that to foster broad-based growth and job creation in the region, labor market reforms must not be viewed in isolation but rather as a component of a comprehensive program of structural reforms.Environmental Economics&Policies,Banks&Banking Reform,Labor Policies,Economic Theory&Research,Labor Markets,Municipal Financial Management,Environmental Economics&Policies,Labor Markets,Banks&Banking Reform,Economic Theory&Research

Beam Propagation Through Atmospheric Turbulence Using an Altitude-Dependent Structure Profile with Non-Uniformly Distributed Phase Screens

For free-space optical communication systems, numerical wave optics simulations provide a useful technique for modeling turbulence-induced beam degradation when the analytical theory is insufficient for characterizing the atmospheric channel. Motivated by such applications we use a split-step method modeling the turbulence as a series of random phase screens using the Hufnagel-Valley turbulence profile. We employ a space-to-ground case study to examine the irradiance and phase statistics for uniformly and non-uniformly located screens and find better agreement with theory using a non-uniform discretization minimizing the contribution of each screen to the total scintillation. In this poster, we summarize the method and the results of the case study including a comparison to layered models used in astronomical imaging applications

Beam Propagation Through Atmospheric Turbulence Using an Altitude-Dependent Structure Profile with Non-Uniformly Distributed Phase Screens

Modeling the effects of atmospheric turbulence on optical beam propagation is a key element in the design and analysis of free-space optical communication systems. Numerical wave optics simulations provide a particularly useful technique for understanding the degradation of the optical field in the receiver plane when the analytical theory is insufficient for characterizing the atmospheric channel. Motivated by such an application, we use a split-step method modeling the turbulence along the propagation path as a series of thin random phase screens with modified von Karman refractive index statistics using the Hufnagel-Valley turbulence profile to determine the effective structure constant for each screen. In this work, we employ a space-to-ground case study to examine the irradiance and phase statistics for both uniformly and non-uniformly spaced screens along the propagation path and compare to analytical results. We find that better agreement with the analytical theory is obtained using a non-uniform spacing with the effective structure constant for each screen chosen to minimize its contribution to the scintillation in the receiver plane. We evaluate this method as a flexible alternative to other standard layered models used in astronomical imaging applications

Anisotropy of Alfv\'enic Turbulence in the Solar Wind and Numerical Simulations

We investigate the anisotropy of Alfv\'enic turbulence in the inertial range of slow solar wind and in both driven and decaying reduced magnetohydrodynamic simulations. A direct comparison is made by measuring the anisotropic second-order structure functions in both data sets. In the solar wind, the perpendicular spectral index of the magnetic field is close to -5/3. In the forced simulation, it is close to -5/3 for the velocity and -3/2 for the magnetic field. In the decaying simulation, it is -5/3 for both fields. The spectral index becomes steeper at small angles to the local magnetic field direction in all cases. We also show that when using the global rather than local mean field, the anisotropic scaling of the simulations cannot always be properly measured.Comment: 9 pages, 8 figure