Users’ willingness to pay for web identity management systems

Electronic services such as virtual communities or electronic commerce demand user authentication. Several more or less successful federated identity management systems have emerged to support authentication across diverse service domains in recent years. In this paper, we explore the determinants for success and failure of such systems with a focus on Germany representing one of the largest markets in Europe. To achieve this goal, we analyze the preferences and willingness to pay of prospective users by conducting a choice-based conjoint analysis. Our results indicate that users prefer simple systems where an intermediary takes care of their data. An additional market analyses confirms these findings and contradicts the assumptions of many researchers, especially in the fields of engineering and computer science, supporting systems with higher and higher levels of privacy and security.     

Heuristic decision rules for short-term trading of renewable energy with co-located energy storage

In the last decade, the share of renewable energy sources in the energy mix has risen significantly in many countries, and the large-scale integration of these intermittent energy sources constitutes a major challenge to the power grid. A crucial building block of a successful transformation of today's energy systems is the use of energy storage, either co-located with renewable energy sources or on a grid-level.To this end, we present a model on the basis of a Markov Decision Process for the short-term trading of intermittent energy production co-located with energy storage. The model explicitly considers the time lag between trade and delivery of energy, which is characteristic for energy markets. Our storage representation includes asymmetrical conversion losses, asymmetrical power, and self-discharge. Stochastic production and market prices are represented by ARIMA processes, and the producer may also undertake price arbitrage by purchasing energy on the market when prices are comparatively low.Regarding the solution of our model, we develop several intuitive and easily interpretable decision rules that can be readily applied in practice. An extensive numerical study, based on real-world data, confirms the excellent performance of these rules in comparison to a sophisticated Approximate Dynamic Programming algorithm adapted from literature.     

基于联盟区块链的安全能源交易方案

目前区块链技术广泛应用于车载网、能源互联网、智能电网等领域，但攻击者可以结合社会工程学与数据挖掘算法获取用户记录在区块链网络中的隐私数据，尤其是微网中相邻能源节点之间由于博弈产生的数据更容易导致隐私的泄露。为了解决这一安全问题，基于联盟区块链技术提出一个以一对多的能源节点账户匹配机制为核心的安全能源互联网交易模型。该模型主要通过新账户的生成来防止攻击者通过数据挖掘算法从交易记录中获取能源节点的账户、地理位置、能源使用情况等隐私数据。仿真实验结合联盟链的特点、能源节点新账户生成数量、交易验证时间变化情况，给出对隐私保护性能、交易效率、安全性效率的分析结果。实验结果表明，所提模型在交易发起和验证阶段所需时间较少，具有较高的安全性，且模型能对相邻用户间的交易趋势进行隐藏。所提方案能够很好地适用于能源互联网交易场景。     

Trusted data sharing with flexible access control based on blockchain

Along with the progress of cloud service, a growing quantity of data owners store their data on cloud databases, which can not only reduce data owners’ storage cost but also provide a quick search function. However, while cloud storage brings some conveniences to users, new privacy problems may emerge, such as the leakage of data privacy and user’s query privacy. The best way of protecting data privacy is to encrypt the data. So how to efficiently retrieve the ciphertext to make it available becomes a hot issue in recent years. In this paper, new searchable encryption with multiple keywords is described, it can improve the accuracy of retrieval results, and we present a secure and trusted data sharing framework based on attribute-based encryption (ABE), searchable encryption, and blockchain. Unlike the previous studies, we realize flexible data sharing by using ABE. Furthermore, we transfer the related calculation of ciphertext retrieval to blockchain for credible execution without relying on any trusted third party. The security analysis proves that our method meets the proposed security requirements of data, keyword index, trapdoor, and query. Finally, the experimental results indicate that our scheme suggested has certain practicability and efficiency.     

考虑共享电池站和温控负荷响应的综合能源系统优化调度

针对区域综合能源系统协同优化调度问题,构建基于能量枢纽(energy hub,EH)的分层能量管理框架,充分考虑新能源电厂、气-电、电-气能源转换装置调节能力,以共享电池站(shared battery station,SBS)替代传统储能电站,为能源系统提供电能存储功能,采用温控负荷为能源系统提供需求侧响应,保障异质...     

Exploring consumer preferences in cloud archiving – a student's perspective

Cloud storage has seen an increasing rise in demand and diffusion. Consequently, the cloud storage market is also becoming an increasingly commoditised market. That is, homogenous products are offered at equal prices, and this offer makes it more difficult for cloud storage providers to generate revenue and differentiate themselves from their competitors. Therefore, it is vital for providers to precisely understand customer preferences so that these can be targeted with appropriate services. To examine these preferences, we conduct a choice experiment and analyse choice decisions gathered from 340 German students by means of a conjoint analysis. We perform an individual-level analysis of preferences, which reveals significant differences and heterogeneity within the sample. By using a subsequent cluster analysis, we identify three distinct customer segments that also show significant differences in, for example, the perceptions of information privacy and risks. Our findings contribute to the literature by uncovering the preference structure and trade-offs that users make in their choice of storage services when employed for the purpose of archiving. We conclude the study with a discussion of practical implications that can aid cloud storage providers in service design decisions, and highlight the limitations associated with our research approach and drawn sample.     

An economic model of optimal fraud control and the aftermarket for security services in online marketplaces

The anonymity of online markets allows traders to easily behave opportunistically. Online marketplaces can lower the uncertainty of participants’ identities by adopting preventative controls such as privacy disclosure rules. However, the use of severe privacy controls to engender risk-free environments might sacrifice not only the size of transactions in the marketplace but also the demand for optional security services like escrow services, which constitute a very sizable revenue source for the marketplace services provider. In this vein, we investigate the probability that an integrated online marketplace (IOM) with security services strategically adjusts privacy controls to incentivize traders to self-select both basic transactions and optional security services. Our results show that an integrated marketplace increases the probability of allowing more fraud than is socially optimal by lowering privacy controls. Market risk can be viewed as an asset for an integrated marketplace rather than a liability that inflicts transaction costs on worried traders. Our study argues that marketplaces may differ in terms of their fraud control from what is socially optimal, according to their revenue structures so that the control of online fraud needs to be regulated from the social perspective. However, under certain conditions, integration of this aftermarket will not harm traders or the social welfare.     

LEARNING OLIGOPOLISTIC COMPETITION IN ELECTRICITY AUCTIONS

This paper addresses the problem of auction markets efficiency within the context of recently liberalized electricity markets. Two different auction mechanisms, i.e., the uniform and the discriminatory price setting rules, have been employed worldwide in designing electricity markets. In this paper, we study the relative efficiency of the two auction mechanisms in the framework of the learning-in-games approach. The behavior of electricity suppliers are modeled by means of an adaptive learning algorithm and the demand is assumed to be constant and inelastic, according to a common hypothesis in electricity market modeling. Computational experiments results are interpreted according game theoretical solutions, i.e., Nash equilibria and Pareto optima. Different economic scenarios corresponding to a duopoly and a tripoly competition with different level of demand are considered. Results show that in the proposed conditions, sellers learn to play competitive strategies, which correspond to Nash equilibria. Finally, this study establishes that, in the presented computational setting and economic scenarios, the discriminatory auction mechanism results more efficient than the uniform auction one.     

The disclosure of private data: measuring the privacy paradox in digital services

Privacy is a current topic in the context of digital services because such services demand mass volumes of consumer data. Although most consumers are aware of their personal privacy, they frequently do not behave rationally in terms of the risk-benefit trade-off. This phenomenon is known as the privacy paradox. It is a common limitation in research papers examining consumers’ privacy intentions. Using a design science approach, we develop a metric that determines the extent of consumers’ privacy paradox in digital services based on the theoretical construct of the privacy calculus. We demonstrate a practical application of the metric for mobile apps. With that, we contribute to validating respective research findings. Moreover, among others, consumers and companies can be prevented from unwanted consequences regarding data privacy issues and service market places can provide privacy-customized suggestions.     

基于动态累加器的去中心化加密搜索方案

近年来区块链技术取得广泛关注,涌现出众多基于区块链技术的新型应用,其中以 StorJ、Filecoin为代表的去中心化存储应用取得了较好的市场反响。对比传统中心化存储,去中心化存储为用户提供了全新的数据存储思路,令用户在获得更好的服务伸缩性的同时,有效降低数据存储的成本。但在现有的去中心化存储方案中,用户的隐私不能得到有效保护。基于此,介绍了一种利用加密搜索技术对去中心化存储方案进行加强的方法。新方法将动态累加器算法引入加密搜索过程中,保障用户存储内容隐私并提供了更好的加密搜索性能。     

Re-identification of Smart Meter data

The Smart Grid approach enhances the power grid with information technology. Smart Meters are an important part of the Smart Grid. They record the energy consumption of households with a high-resolution and transfer consumption records to the energy provider in real time. Since they allow to infer personal information like the daily routine of the household members, Smart Meters are also a promising source for lifelogging. However, in liberalized energy markets, many different parties have access to these data. This puts the privacy of consumers at risk. In this paper, we analyze to which degree Smart Meter data, as collected by our industry partner, can be linked to its producer, using simple statistical measures. We devise features of the energy consumption, for example, the first peak of demand in the morning, and we describe an analytical framework that quantifies how well these features can identify households. Finally, we conduct a study with 60,480 energy-consumption records from 180 households. Our study shows that 68 % of the records can be re-identified with simple means already. This insight is important for Smart Grids, as it emphasizes the need for research and use of anonymization techniques for the Smart Grid.     

Application-Aware Client-Side Data Reduction and Encryption of Personal Data in Cloud Backup Services

Cloud backup has been an important issue ever since large quantities of valuable data have been stored on the personal computing devices. Data reduction techniques, such as deduplication, delta encoding, and Lempel-Ziv （LZ） compression, performed at the client side before data transfer can help ease cloud backup by saving network bandwidth and reducing cloud storage space. However, client-side data reduction in cloud backup services faces efficiency and privacy challenges. In this paper, we present Pangolin, a secure and efficient cloud backup service for personal data storage by exploiting application awareness. It can speedup backup operations by application-aware client-side data reduction technique, and mitigate data security risks by integrating selective encryption into data reduction for sensitive applications. Our experimental evaluation, based on a prototype implementation, shows that our scheme can improve data reduction efficiency over the state-of-the-art methods by shortening the backup window size to 33%-75%, and its security mechanism for＇ sensitive applications has negligible impact on backup window size.     

Multiple households energy consumption forecasting using consistent modeling with privacy preservation

Traditional data-driven energy consumption forecasting models, including machine learning and deep learning methods, showed outstanding performance in terms of forecasting accuracy and efficiency. The superior performances are based on enough training data samples. Moreover, the derived forecasting model is only applicable to the training dataset and usually is applied to specific household. In real-world smart city development, a centralized forecasting model is required to model and forecasting energy consumption patterns for multiple households, whereas the traditional data-driven forecasting approaches may become invalid. A consistent model is demanded in this scenario modeling multiple households’ energy consumption patterns. Additionally, privacy issues are also highly concerned in such scenarios. Accurate energy consumption forecasting with privacy preservations becomes a key point for the state-of-art research. In this study, we adopt an innovative privacy-preserving structure that combines deep learning and federated learning. Under the premise of guaranteeing forecasting accuracy and privacy preservation, this structure can achieve the forecasting of various household energy consumption with a consistent model that simultaneously forecast multiple household energy consumption data by transmission control protocol.     

复杂式网络用户隐私数据多层分类存储仿真

为了确保复杂式网络用户隐私数据多层分类存储的完整性和有效性,针对当前方法数据存储的不完整和数据存储时间长的问题,提出基于贝叶斯的复杂式网络用户隐私数据多层分类存储方法。为了提升用户隐私数据多层分类存储的效果,通过对用户隐私数据权重的计算建立数据多层分类存储模型,在此基础上,分析用户隐私数据的特征向量和数据类别向量的联合分布情况,根据分布结果和概率分类器计算用户隐私数据的条件概率分布,并分析数据的对数似然概率,得到用户隐私的训练数据和测试数据,对数据类别展开预测,完成用户隐私数据的多层分类,利用数据文件的分块数、副本系数以及用户隐私数据最小的副本系数目标函数与数据分类结果相结合,最终实现用户隐私数据多层分类存储。实验结果表明,提出方法能够完整存储用户隐私数据,并且数据存储时间较短,降低了用户隐私数据多层分类存储能耗,验证了提出方法的有效性。     

The catallaxy approach for decentralized economic-based allocation in Grid resource and service markets

Efficient resource allocation in dynamic large-scale environments is one of the challenges of Grids. In centralized economic-based allocation approaches, the user requests can be matched to the fastest, cheapest or most available resource. This approach, however, shows limitations in scalability and in dynamic environments. In this paper, we explore a decentralized economic approach for resource allocation in Grid markets based on the Catallaxy paradigm. Catallactic agents discover selling nodes in the resource and service Grid markets, and negotiate with each other maximizing their utility by following a strategy. By means of simulations, we evaluate the behavior of the approach, its resource allocation efficiency and its performance with different demand loads in a number of Grid density and dynamic environments. Our results indicate that while the decentralized economic approach based on Catallaxy applied to Grid markets shows similar efficiency to a centralized system, its decentralized operation provides greater advantages: scalability to demand and offer, and robustness in dynamic environments.     

Progressive equilibration algorithms: The case of linear transaction costs

In this paper we consider the solution of large-scale market equilibrium problems with linear transaction costs which can be formulated as strictly convex quadratic programming problems, subject to supply and demand constraints. In particular, we introduce two new classes of progressive equilibration algorithms, which retain the simplicity of the original cyclic ones in that at each step either the supply or demand market equilibrium subproblem can be solved explicitly in closed form. However, rather than equilibrating the markets in cyclic manner, the next market to be equilibrated is selected in a more strategic fashion.We then provide qualitative results for the entire family of progressive equilibration algorithms, i.e., the rate of convergence and computational complexity. We discuss implementation issues and give computational results for large-scale examples in order to illustrate and give insights into the theoretical analysis. Furthermore, we show that one of the new classes of algorithms, the good-enough one, is computationally the most efficient. Theoretical results are important in that the relative efficiency of different algorithms need no longer be language, machine, or programmer dependent. Instead, the theory can guide both practitioners and researchers in ensuring that their implementation of these algorithms is, indeed, good.Since an equivalent quadratic programming problem arises in a certain class of constrained matrix problems, our results can be applied there, as well. Finally, since more general asymmetric multicommodity market equilibrium problems can be solved as series of the type of problems considered here, the result$ are also applicable to such equilibrium problems.     

一种共享数据完整性验证方案的分析与改进

首先分析了一种云计算环境下公开可验证的共享数据完整性验证方案,指出其无法抵抗数据恢复攻击和完整性伪造攻击,从而存在数据泄露和完整性欺骗的风险。在此基础上提出一个安全性增强的改进方案并对其进行了安全性分析和性能开销分析,分析结果表明该方案保持了数据存储安全性和隐私保护性,并且能够高效实现共享数据的完整性验证,具备较高的安全性和验证效率。     

Recommender system for privacy-preserving solutions in smart metering

Nowadays, Smart Grid is envisaged to provide several benefits to both customers and grid operators. However, Smart Meters introduce many privacy issues if consumption data is analysed. In this paper we analyse the main techniques that address privacy when collecting electricity readings. In addition to privacy, it is equally important to preserve efficiency to carry on with monitoring operations, so further control requirements and communication protocols are also studied. Our aim is to provide guidance to installers who intend to integrate such mechanisms on the grid, presenting an expert system to recommend an appropriate deployment strategy.     

面向边缘计算应用的宽度孪生网络

边缘计算是将计算、存储、通信等任务分配到网络边缘的计算模式. 它强调在用户终端附近执行数据处理过程, 以达到降低延迟, 减少能耗, 保护用户隐私等目的. 然而网络边缘的计算、存储、能源资源有限, 这给边缘计算应用的推广带来了新的挑战. 随着边缘智能的兴起, 人们更希望将边缘计算应用与人工智能技术结合起来, 为我们的生活带来更多的便利. 许多人工智能方法, 如传统的深度学习方法, 需要消耗大量的计算、存储资源, 并且伴随着巨大的时间开销. 这不利于强调低延迟的边缘计算应用的推广. 为了解决这个问题, 我们提出将宽度学习系统(Broad learning system, BLS)等浅层网络方法应用到边缘计算应用领域, 并且设计了一种宽度孪生网络算法. 我们将宽度学习系统与孪生网络结合起来用于解决分类问题. 实验结果表明我们的方法能够在取得与传统深度学习方法相似精度的情况下降低时间和资源开销, 从而更好地提高边缘计算应用的性能.