A critical overview of privacy-preserving approaches for collaborative forecasting

Cooperation between different data owners may lead to an improvement in forecast quality—for instance, by benefiting from spatiotemporal dependencies in geographically distributed time series. Due to business competitive factors and personal data protection concerns, however, said data owners might be unwilling to share their data. Interest in collaborative privacy-preserving forecasting is thus increasing. This paper analyzes the state-of-the-art and unveils several shortcomings of existing methods in guaranteeing data privacy when employing vector autoregressive models. The methods are divided into three groups: data transformation, secure multi-party computations, and decomposition methods. The analysis shows that state-of-the-art techniques have limitations in preserving data privacy, such as (i) the necessary trade-off between privacy and forecasting accuracy, empirically evaluated through simulations and real-world experiments based on solar data; and (ii) iterative model fitting processes, which reveal data after a number of iterations.     

Guarded dual authentication based DRM with resurgence dynamic encryption techniques

Cloud computing is the emergent technology that face one of the significant issues time with data security while outsourcing the data onto the cloud in recent. Some cryptographic techniques have been used for protection in form of identity, attributes and prediction algorithms nonetheless these algorithms lack their performance and becomes are very prone to attackers when an unauthorized user reunited the system with dissimilar way for privileges to the similar data files. The essential need of this data security solved by some enhanced cryptographic techniques in DRM utilizing a secure privacy preserving data sharing with encryption techniques of Dynamic Unidirectional Proxy Re-Encryption. This technique is based on Cipher text Policy Attribute by providing the privacy, integrity and security of the data while retrieving.     

Online adaptive lasso estimation in vector autoregressive models for high dimensional wind power forecasting

Wind power forecasts with lead times of up to a few hours are essential to the optimal and economical operation of power systems and markets. Vector autoregression (VAR) is a framework that has been shown to be well suited to predicting for several wind farms simultaneously by considering the spatio-temporal dependencies in their time series. Lasso penalisation yields sparse models and can avoid overfitting the large numbers of coefficients in higher dimensional settings. However, estimation in VAR models usually does not account for changes in the spatio-temporal wind power dynamics that are related to factors such as seasons or wind farm setup changes, for example. This paper tackles this problem by proposing a time-adaptive lasso estimator and an efficient coordinate descent algorithm for updating the VAR model parameters recursively online. The approach shows good abilities to track changes in the multivariate time series dynamics on simulated data. Furthermore, in two case studies it shows clearly better predictive performances than the non-adaptive lasso VAR and univariate autoregression.     

我国荒漠地区利用清洁能源发电的潜力与展望

我国荒漠地区范围广,光照充分、风力强大,且干旱少雨,不利于农耕,但这些区域具有发展太阳能发电、风能发电、生物质能发电和垃圾焚烧发电等多种发电形式的潜力。荒漠地区地势平坦,有利于交通运输;草木矮小裸地,有利于风力流动;人烟稀少,有利于垃圾焚烧。合理开发利用这块热土,用绿色能源发电,从而缓解化石能源利用的紧张,是文章重点讨论的问题。     

Spatiotemporal wind forecasting by learning a hierarchically sparse inverse covariance matrix using wind directions

Given the advances in online data acquisition systems, statistical learning models are increasingly used to forecast wind speed. In electricity markets, wind farm production forecasts are needed for the day-ahead, intra-day, and real-time markets. In this work, we use a spatiotemporal model that leverages wind dynamics to forecast wind speed. Using a priori knowledge of the wind direction, we propose a maximum likelihood estimate of the inverse covariance matrix regularized with a hierarchical sparsity-inducing penalty. The resulting inverse covariance estimate not only exhibits the benefits of a sparse estimator, but also enables meaningful sparse structures by considering wind direction. A proximal method is used to solve the underlying optimization problem. The proposed methodology is used to forecast six-hour-ahead wind speeds in 20-minute time intervals for a case study in Texas. We compare our method with a number of other statistical methods. Prediction performance measures and the Diebold–Mariano test show the potential of the proposed method, specifically when reasonably accurate estimates of the wind directions are available.     

一种人工神经网络样本数据的对数螺旋线扰动方法

通过构建面向聚类的隐私保护数据扰动模型,利用对数螺线对原始数据进行扰动隐藏,维持原始数据的邻域关系稳定,实现数据集聚类可用性的有效维护;进一步提高BP神经网络的收敛速度,并且能够有效地避免数据隐私泄露,同时维持输出结果的可用性。     

A Survey of Differentially Private Regression for Clinical and Epidemiological Research

Differential privacy is a framework for data analysis that provides rigorous privacy protections for database participants. It has increasingly been accepted as the gold standard for privacy in the analytics industry, yet there are few techniques suitable for statistical inference in the health sciences. This is notably the case for regression, one of the most widely used modelling tools in clinical and epidemiological studies. This paper provides an overview of differential privacy and surveys the literature on differentially private regression, highlighting the techniques that hold the most relevance for statistical inference as practiced in clinical and epidemiological research. Research gaps and opportunities for further inquiry are identified.     

Thematic content analysis using supervised machine learning: An empirical evaluation using German online news

In recent years, two approaches to automatic content analysis have been introduced in the social sciences: semantic network analysis and supervised text classification. We argue that, although less linguistically sophisticated than semantic parsing techniques, statistical machine learning offers many advantages for applied communication research. By using manually coded material for training, supervised classification seamlessly bridges the gap between traditional and automatic content analysis. In this paper, we briefly introduce the conceptual foundations of machine learning approaches to text classification and discuss their application in social science research. We then evaluate their potential in an experimental study in which German online news was coded with established thematic categories. Moreover, we investigate whether and how linguistic preprocessing can improve classification quality. Results indicate that supervised text classification is generally robust and reliable for some categories, but may even be useful when it fails.     

风力发电机接入直流微电网的运行控制研究

本文针对目前现行风力发电机系统接入微网效率低和不易控制的问题,提出建立改进型的风力发电模型,这个系统主要包括:风力机、永磁同步发电机、三相整流桥、DC-DC变换器以及蓄电池。利用PSIM仿真软件进行等效仿真验证。仿真结果表明风力发电机通过整流变换后能很好的能接入直流微电网,为用户提供高质量的电能。     

Reasoning under uncertainty and multi-criteria decision making in data privacy

By means of an integration of decision theory and probabilistic models, we explore and develop methods for improving data privacy. Our work encompasses disclosure control tools in statistical databases and privacy requirements prioritization; in particular we propose a Bayesian approach for the on-line auditing in Statistical Databases and Pairwise Comparison Matrices for privacy requirements prioritization. The first approach is illustrated by means of examples in the context of statistical analysis on the census and medical data, where no salary (resp. no medical information), that could be related to a specific employee (resp. patient), must be released; the second approach is illustrated by means of examples, such as an e-voting system and an e-banking service that have to satisfy privacy requirements in addition to functional and security ones. Several fields in the social sciences, economics and engineering will benefit from the advances in this research area: e-voting, e-government, e-commerce, e-banking, e-health, cloud computing and risk management are a few examples of applications for the findings of this research.     

The Importance of Communicating Workplace Privacy Policies

Employers today are faced with the task of managing workplace privacy, dealing with potential litigation, preserving the confidentiality of company information while protecting the rights of the employee. Since September 11, 2001, employers have faced new challenges including the implementation of new laws and the development of more sophisticated technology. As a result, workplace policies dealing with privacy must be clearly and effectively communicated to all affected workers. The objective of this survey research project was to measure the general public’s attitudes and perceptions of the communication of work place privacy policies. An emphasis was placed on the collection of real world data from multiple business and organizational environments, as opposed to data obtained in a controlled experimental setting.     

Physics-informed Gaussian process regression for states estimation and forecasting in power grids

Real-time state estimation and forecasting are critical for the efficient operation of power grids. In this paper, a physics-informed Gaussian process regression (PhI-GPR) method is presented and used for forecasting and estimating the phase angle, angular speed, and wind mechanical power of a three-generator power grid system using sparse measurements. In standard data-driven Gaussian process regression (GPR), parameterized models for the prior statistics are fit by maximizing the marginal likelihood of observed data. In the PhI-GPR method, we propose to compute the prior statistics offline by solving stochastic differential equations (SDEs) governing the power grid dynamics. The short-term forecast of a power grid system dominated by wind generation is complicated by the stochastic nature of the wind and the resulting uncertainty in wind mechanical power. Here, we assume that the power grid dynamics are governed by swing equations, with the wind mechanical power fluctuating randomly in time. We solve these equations for the mean and covariances of the power grid states using the Monte Carlo simulation method.We demonstrate that the proposed PhI-GPR method can accurately forecast and estimate observed and unobserved states. For the considered problem, PhI-GPR has computational advantages over the ensemble Kalman filter (EnKF) method: In PhI-GPR, ensembles are computed offline and independently of the data acquisition process, whereas for EnFK, ensembles are computed online with data acquisition, rendering real-time forecast more challenging. We also demonstrate that the PhI-GPR forecast is more accurate than the EnKF forecast when the random mechanical wind power is non-Markovian. In contrast, the two methods produce similar forecasts for the Markovian mechanical wind power.For observed states, we show that PhI-GPR provides a forecast comparable to the standard data-driven GPR; both forecasts are significantly more accurate than the autoregressive integrated moving average (ARIMA) forecast. We also show that the ARIMA forecast is more sensitive to observation frequency and measurement errors than the PhI-GPR forecast.     

Short‐Term Wind Speed Forecasting for Power System Operations

The emphasis on renewable energy and concerns about the environment have led to large‐scale wind energy penetration worldwide. However, there are also significant challenges associated with the use of wind energy due to the intermittent and unstable nature of wind. High‐quality short‐term wind speed forecasting is critical to reliable and secure power system operations. This article begins with an overview of the current status of worldwide wind power developments and future trends. It then reviews some statistical short‐term wind speed forecasting models, including traditional time series approaches and more advanced space–time statistical models. It also discusses the evaluation of forecast accuracy, in particular, the need for realistic loss functions. New challenges in wind speed forecasting regarding ramp events and offshore wind farms are also presented.     

DPWeVote: differentially private weighted voting protocol for cloud-based decision-making

ABSTRACT

With the advent of Industry 4.0, cloud computing techniques have been increasingly adopted by industry practitioners to achieve better workflows. One important application is cloud-based decision-making, in which multiple enterprise partners need to arrive an agreed decision. Such cooperative decision-making problem is sometimes formed as a weighted voting game, in which enterprise partners express ‘YES/NO’ opinions. Nevertheless, existing cryptographic approaches to Cloud-Based Weighted Voting Game have restricted collusion tolerance and heavily rely on trusted servers, which are not always available. In this work, we consider the more realistic scenarios of having semi-honest cloud server/partners and assuming maximal collusion tolerance. To resolve the privacy issues in such scenarios, the DPWeVote protocol is proposed which incorporates Randomized Response technique and consists the following three phases: the Randomized Weights Collection phase, the Randomized Opinions Collection phase, and the Voting Results Release phase. Experiments on synthetic data have demonstrated that the proposed DPWeVote protocol managed to retain an acceptable utility for decision-making while preserving privacy in semi-honest environment.     

Online Principal Component Analysis in High Dimension: Which Algorithm to Choose?

Principal component analysis (PCA) is a method of choice for dimension reduction. In the current context of data explosion, online techniques that do not require storing all data in memory are indispensable to perform the PCA of streaming data and/or massive data. Despite the wide availability of recursive algorithms that can efficiently update the PCA when new data are observed, the literature offers little guidance on how to select a suitable algorithm for a given application. This paper reviews the main approaches to online PCA, namely, perturbation techniques, incremental methods and stochastic optimisation, and compares the most widely employed techniques in terms statistical accuracy, computation time and memory requirements using artificial and real data. Extensions of online PCA to missing data and to functional data are detailed. All studied algorithms are available in the  package onlinePCA on CRAN.     

Online privacy and security practices of the 100 largest US nonprofit organizations

• Although online consumer privacy has been an important issue in the commercial realm for more than a decade, nonprofit organizations (NPOs, or nonprofits, for short) have just begun to address the topic recently. No published scholarly research has examined the online information practices of the largest NPOs with regard to privacy and security issues. The absence of data leaves one unable to empirically gauge the extent of NPO compliance with the Federal Trade Commissions (FTCs) suggested information practices. Such an investigation would be useful not only to US nonprofits but also non‐US nonprofits that are reaching US donors via their web sites.
• This study examines the online information practices of The Nonprofit Times 100 web sites and compares their practices to that of their commercial counterparts. The NPO web sites were found to collect just as much, and in some cases even more, personally identifying information as the commercial sites. The NPO web sites were more likely to display a privacy disclosure and privacy seal. Of critical concern, and not assessed in the commercial samples, is that nearly all of the NPO sites post personally identifying information (of individuals who are not employees).
• The current study provides benchmarks useful for assessing security issues pertaining to the collection, use, and even posting of personal information for NPO web sites. It also proposes actions for improving online security and privacy with the hope of encouraging more discussion of these important issues within the NPO community.

Copyright © 2008 John Wiley & Sons, Ltd.     

探究分析风能发电的现状和未来发展趋势

因为风能具有可再生、无污染以及蕴量比较大等一些优点,所以国际电力的发展趋势就是利用和发展风能等一些可再生资源,在未来的发展过程当中,风能发电肯定会具有更加广阔的前景。本文主要就对风能发电的现状以及未来的发展趋势进行了探究和分析。     

Machine learning in M4: What makes a good unstructured model?

The Makridakis Competitions seek to identify the most accurate forecasting methods for different types of predictions. The M4 competition was the first in which a model of the type commonly described as “machine learning” has outperformed the more traditional statistical approaches, winning the competition. However, many approaches that were self-labeled as “machine learning” failed to produce accurate results, which generated discussion about the respective benefits and drawbacks of “statistical” and “machine learning” approaches. Both terms have remained ill-defined in the context of forecasting. This paper introduces the terms “structured” and “unstructured” models to better define what is intended by the use of the terms “statistical” and “machine learning” in the context of forecasting based on the model’s data generating process. The mechanisms that underlie specific challenges to unstructured modeling are examined in the context of forecasting, along with common solutions. Finally, the innovations in the winning model that allowed it to overcome these challenges and produce highly accurate results are highlighted.     

Modern Strategies for Time Series Regression

This paper discusses several modern approaches to regression analysis involving time series data where some of the predictor variables are also indexed by time. We discuss classical statistical approaches as well as methods that have been proposed recently in the machine learning literature. The approaches are compared and contrasted, and it will be seen that there are advantages and disadvantages to most currently available approaches. There is ample room for methodological developments in this area. The work is motivated by an application involving the prediction of water levels as a function of rainfall and other climate variables in an aquifer in eastern Australia.     

Are You All Normal? It Depends!

The assumption of normality has underlain much of the development of statistics, including spatial statistics, and many tests have been proposed. In this work, we focus on the multivariate setting and first review the recent advances in multivariate normality tests for i.i.d. data, with emphasis on the skewness and kurtosis approaches. We show through simulation studies that some of these tests cannot be used directly for testing normality of spatial data. We further review briefly the few existing univariate tests under dependence (time or space), and then propose a new multivariate normality test for spatial data by accounting for the spatial dependence. The new test utilises the union-intersection principle to decompose the null hypothesis into intersections of univariate normality hypotheses for projection data, and it rejects the multivariate normality if any individual hypothesis is rejected. The individual hypotheses for univariate normality are conducted using a Jarque–Bera type test statistic that accounts for the spatial dependence in the data. We also show in simulation studies that the new test has a good control of the type I error and a high empirical power, especially for large sample sizes. We further illustrate our test on bivariate wind data over the Arabian Peninsula.