A Privacy Protection Evaluation Mechanism for Dynamic Data Based on Chunk-Confusion

Nowadays big data security plays a major issue in cloud computing. Chunk-confusion-based privacy protection mechanism (CCPPM) protects the privacy of the tenants in plaintext. But both multi-tenant applications’ data and tenants’ privacy requirements are dynamically changing, which will have a great effect on the underlying storage model of cloud data. Moreover, the tenants’ business processing will change the data distribution and destroy the distribution balance of privacy data, which makes the data stored in the cloud face the risk of leakage of privacy. Therefore, the paper proposed a privacy protection evaluation mechanism for dynamic data based on CCPPM. The paper firstly introduces three kinds of the privacy leakages due to unbalanced data under the CCPPM, and analyzes two methods used for attacking. Aiming at the privacy leakages and the attack methods, we proposed a dynamic data processing algorithm to record the tenants’ operation sequence and set up the corresponding evaluation formula. Next, we evaluated the effect of privacy protection from two aspects of simple attack and background-knowledge-based attack, and used the data distribution similarity privacy preserving dynamic evaluation algorithm presented in this paper to obtain the measurement results of privacy leakages. Finally, according to the evaluation results, the defense strategies are given to prevent data privacy leakages. The experimental evaluation proves that rationality of dynamic the evaluation mechanism proposed in this paper has better feasibility and practicality for big data privacy protection.     

Local Dependence Test Between Random Vectors Based on the Robust Conditional Spearman’s ρ and Kendall’s τ

Acta Mathematicae Applicatae Sinica, English Series - This paper introduces two local conditional dependence matrices based on Spearman’s ρ and Kendall’s τ given the condition...     

Tunable Probes with Direct Fluorescence Signals for the Constitutive and Immunoproteasome

Electrophiles are commonly used for the inhibition of proteases. Notably, inhibitors of the proteasome, a central determinant of cellular survival and a target of several FDA‐approved drugs, are mainly characterized by the reactivity of their electrophilic head groups. We aimed to tune the inhibitory strength of peptidic sulfonate esters by varying the leaving groups. Indeed, proteasome inhibition correlated well with the pK_a of the leaving group. The use of fluorophores as leaving groups enabled us to design probes that release a stoichiometric fluorescence signal upon reaction, thereby directly linking proteasome inactivation to the readout. This principle could be applicable to other sulfonyl fluoride based inhibitors and allows the design of sensitive probes for enzymatic studies.     

Wander of a Gaussian-Beam Wave Propagated Through a Non-Kolmogorov Turbulent Atmosphere

Both increasing experimental evidence and some results of theoretical investigation have shown that there exist two kinds of turbulence in the aerosphere, Kolmogorov and non-Kolmogorov turbulence. Thus, it is necessary to improve the theory of optical wave propagation through atmospheric turbulence, namely, study the laser-beam propagation in non-Kolmogorov turbulence, before analyzing the joint influence of the Kolmogorov turbulence and non-Kolmogorov one on satellite laser communication. The beam wander will lead to the performance degradation of satellite laser communication systems and exert an influence on the achievement and stability of its links. In this paper, we consider a theoretical power spectrum of refractive-index fluctuations with a generalized power law in order to derive the variance of Gaussian-beam wave wander in weak turbulence for a horizonal path and analyze the influence of spectral power-law variations on the beam wander. We show that the expression for the beam-wander variance is of concise closed form and independent of the optical wavelength.     

Phase retrieval from fringe pattern with 1-D discrete wavelet transform

This paper proposes a novel method to obtain frequency modulation (FM) signals from a single fringe pattern for phase retrieval. First, a 1D discrete Meyer wavelet is employed to decompose the pattern image signal row by row and the soft-thresholding approach is applied to remove noise. The low frequency coefficients of the wavelet decomposition are then set to 0, and the signal is reconstructed. Moreover, the optimal wavelet decomposition level is adaptively determined using a cost function-based method. The reconstructed signal, which no longer contains a background component, is normalized using a nonlinear and piecewise normalization method. The proposed method is faster and more accurate than some other phase retrieval approaches, which is illustrated with two test cases.     

阿尔茨海默病诊断的自适应BP神经网络模型

阿尔茨海默病(AD)和轻度认知功能损伤(MCI)具有患者多、诊断难的特点,改进BP神经网络,提出自适应BP神经网络(ABP)进行100次AD和MCI诊断模拟,ABP神经网络的诊断正确率显著高于BP和RBF神经网络.采用留一法将101例正常人、200例MCI和90例AD患者的样本分为训练集和检测集,用ABP神经网络对其进行诊断模拟,总正确率达到73.91%.     

任务驱动法在高校计算机基础的教学研究

随着社会和科技的不断发展,计算机的应用已经很普及,本文针对任务驱动法进行概述,分析了任务驱动法在高校计算机基础课程中的实践,提出了要明确教学目标、及时对学生进行指导、及时进行纠错,发现教学难点、及时进行归纳总结的实践方法。     

基于EOC的有线无线融合覆盖模式研究

三网融合趋势下,有线网络和无线网络融合发展,才能实现多种业态运营,满足用户多样化需求.研究了通过双向接入网EoC结合WiFi的有线无线融合覆盖模式,使用户能够通过电视机、便携式计算机、平板PC、智能手机等多种终端开展有线数字电视的多屏互动业务.同时,探讨性地提出EoC无线转发技术也为双向交互数据业务的有线无线融合覆盖模式提供了新思路.     

无线电管理系统集成项目集成管理

该文从信息系统集成项目管理的基础理论和方法出发,以江苏省无线电管理局电磁频谱监管中心系统集成项目为案例,在充分了解无线电管理系统的现状及项目目标的基础上,分析现有的项目管理方法与手段,明确无线电管理项目在实际进展过程中的具体需求与建设内容,对无线电管理系统集成项目进行分析,为无线电管理系统集成的项目建设提供一定的借鉴作用.     

一种基于异常值检测和群体智能优化神经网络的电价混合预测模型

为了提高短期电价预测精度,本文提出了一种将异常值检测、时间序列分析、神经网络以及群体智能算法相结合的混合算法。作为混合算法的具体实现,文中的异常值检测利用了残差比方法和正态分布方法,群体智能优化算法选取了粒子群(PSO)算法和布谷鸟(CS)算法。作为实例研究,本文将混合模型应用用于澳大利亚新南威尔士州短期电价预测中,结果表明,混合预测方法能在一定程度上提高模型的预测精度。