Blockchain Based Smart Auction Mechanism for Distributed Peer-to-Peer Energy Trading

Blockchain based framework provides data immutability in a distributed network. In this paper, we investigate the application of blockchain for peer-to-peer (P2P) energy trading. Traditional energy trading systems use simple passing mechanisms and basic pricing methods, thus adversely affect the efficiency and buyers’ social welfare. We propose a blockchain based energy trading mechanism that uses smart passing of unspent auction reservations to (a) minimise the time taken to settle an auction (convergence time), (b) maximise the number of auction settlement; and (c) incorporate second-price auction pricing to maximise buyers’ social welfare in a distributed double auction environment. The entire mechanism is implemented within Hyperledger Fabric, an open-source blockchain framework, to manage the data and provide smart contracts. Experiments show that our approach minimises the convergence time, maximises the number of auction settlement, and increases the social welfare of buyers compared to existing methods

A deep dive into the accuracy of IP geolocation databases and its impact on online advertising

The quest for every time more personalized Internet experience relies on the enriched contextual information about each user. Online advertising also follows this approach. Among the context information that advertising stakeholders leverage, location information is certainly one of them. However, when this information is not directly available from the end users, advertising stakeholders infer it using geolocation databases, matching IP addresses to a position on earth. The accuracy of this approach has often been questioned in the past: however, the reality check on an advertising stakeholder shows that this technique accounts for a large fraction of the served advertisements. In this paper, we revisit the work in the field, that is mostly from almost one decade ago, through the lenses of big data. More specifically, we, i) benchmark two commercial Internet geolocation databases, evaluate the quality of their information using a ground-truth database of user positions containing over 2 billion samples, ii) analyze the internals of these databases, devising a theoretical upper bound for the quality of the Internet geolocation approach, and iii) we run an empirical study that unveils the monetary impact of this technology by considering the costs associated with a real-world ad impressions dataset.This work was supported in part by European Union's Horizon 2020 innovation action programme under the PIMCITY Project under Grant 871370, in part by TESTABLE Project under Grant 101019206, in part by Agencia Estatal de Investigacion (AEI) under the ACHILLES Project under Grants PID2019-104207RB-I00/AEI/10.13039/501100011033, in part by the Spanish Ministry of Economic Affairs and Digital Transformation and European Union-Next GenerationEU through the UNICO 5G I+D 6G-RIEMANN-FR, in part by the agreement between the Community of Madrid and the Universidad Carlos III de Madrid for the funding of research projects on SARS-CoV-2 and COVID-19 disease, through project name Multi-source and multi-method prediction to support COVID-19 policy decision making, which was supported with REACT-EU funds from the European regional development fund a way of making Europe, and in part by TAPTAP-UC3M Chair in advanced AI and Data Science applied to advertising and marketing

Prosper—The eBay for Money in Lending 2.0

When a bank or a credit union turns you down for a loan because your debt-to-income ratio is too high, can you turn to total strangers to get the money? Yes, you can, and we are not talking about criminal acts. It\u27s called peer-to-peer (P2P) lending or social lending. Prosper was the first company to take the vision of social lending and convert it into practice on the Internet in the United States, and it enjoyed fast growth after launch. Four years later, however, it is facing old and new challenges, and its survival is on the line. This case depicts the opportunities and pressures Prosper faced, its actions and reactions, and its future. Prosper has made and will make many important decisions, and Prosper’s successes and challenges are rich material to study

Peer-to-peer lending and community development finance

Peer-to-peer (P2P) networks directly connect computer users online. Popular P2P platforms include eBay and Craigslist, for example, which have transformed the market for used consumer goods in recent years. Increasingly popular, however, are P2P lending sites that facilitate debt transactions by directly connecting borrowers and lenders on the Internet. In the summer of 2008, the Center for Community Development Investments assembled a working group of community development leaders, investors, and Prosper Marketplace, the largest P2P lending platform in the world, to discuss the potential community development implications of the innovation. This working paper documents this discussion and explores P2P lending in greater detail. Part I offers background on P2P and the state of the P2P lending industry; Part II outlines the potential community development finance implications of P2P; and Part III discusses the working group and next steps necessary to successfully marry P2P technology and community development finance.Loans ; Internet

Determinants of Privatization Prices

Generating government revenue is a common objective in privatization. This paper asks: what determines privatization prices? Pursuing this query helps resolve the current controversies about the bearing of speed and the role for government actions prior to privatization. The data, gathered from primary sources, encompass 361 privatized Mexican companies in 49 four-digit industry codes. The determinants of auction privatization prices are divided into three groups: (1) company performance and industry parameters; (2) the auction process and its requirements; and (3) the prior restructuring actions taken by the government. Controlling for company and industry effects reveals the significant impact of the costs and characteristics of the labor force. Minority control packages carry large discounts. Auction requirements that allow foreign investors result in higher sale premia, while restrictions constraining participation or payment forms reduce net prices. The speed of privatization substantially influences net prices: the longer it takes to put the company on the block, the more severe the deterioration in performance, and the lower the premium obtained. Pre-sale reductions in labor force, and particularly the firing of CEOs, lead to significantly higher premiums. Debt absorption, investment, and performance improvement programs do not increase the net price, while de-investment measures prove more beneficial. Overall, the results show increased premia for government actions that stimulate bidder participation and expedite the privatization process.

Auction design and auction outcomes

We study the impact of spectrum auction design on the prices paid by telecommunications operators for two decades across 85 countries. Our empirical strategy combines information about competition in the local market, the level of adoption and a wide range of socio-economic indicators and process specific variables. Using a micro dataset of almost every mobile spectrum auction performed so far—both regional and national—we show that auction design affects final prices paid. Two designs (SMRA with augmented switching and CCA with core pricing) result in auctions with systematically higher normalized returns. Further, we document that spectrum ownership appears to affect prices paid in subsequent auctions. We discuss the mechanisms of cost minimization and foreclosure faced by operators in different regulatory environments. Our findings have implications for policy-makers and regulators

Design, analysis and implementation of advanced methodologies to measure the socio-economic impact of personal data in large online services

El ecosistema web es enorme y, en general, se sustenta principalmente en un atributo intangible que sostiene la mayoría de los servicios gratuitos: la explotación de la información personal del usuario. A lo largo de los años, la preocupación por la forma en que los servicios utilizan los datos personales ha aumentado y atraído la atención de los medios de comunicación, gobiernos, reguladores y también de los usuarios. Esta recogida de información personal es hoy en día la principal fuente de ingresos en Internet. Además, por si fuera poco, la publicidad online es la pieza que lo sustenta todo. Sin la existencia de datos personales en comunión con la publicidad online, Internet probablemente no sería el gigante que hoy conocemos. La publicidad online es un ecosistema muy complejo en el que participan múltiples actores. Es el motor principal que genera ingresos en la red, y en pocos años ha evolucionado hasta llegar a miles de millones de usuarios en todo el mundo. Mientras navegan, los usuarios generan datos muy valiosos sobre sí mismos que los anunciantes utilizan después para ofrecerles productos relevantes en los que podrían estar interesados. Se trata de un enfoque bidireccional, ya que los anunciantes pagan a intermediarios para que muestren anuncios al público que, en principio, está más interesado. Sin embargo, este comercio, intercambio y tratamiento de datos personales, además de abrir nuevas vías de publicidad, exponen la privacidad de los usuarios. Esta incesante recopilación y comercialización de la información personal suele quedar tras un muro opaco, donde el usuario generalmente desconoce para qué se utilizan sus datos. Las iniciativas de privacidad y transparencia se han incrementado a lo largo de los años para empoderar al usuario en este negocio que mueve miles de millones de dólares en ingresos. No en vano, tras varios escándalos, como el de Facebook Cambridge Analytica, las empresas y los reguladores se han unido para crear transparencia y proteger a los usuarios de las malas prácticas derivadas del uso de su información personal. Por ejemplo, el Reglamento General de Protección de Datos, es el ejemplo más prometedor de regulación, que afecta a todos los estados miembros de la Unión Europea, abogando por la protección de los usuarios. El contenido de esta tesis tomará como referencia esta legislación. Por todo ello, el propósito de esta tesis consiste en aportar herramientas y metodologías que pongan de manifiesto usos inapropiados de datos personales por las grandes compañías del ecosistema publicitario online, y cree transparencia entre los usuarios, proporcionando, a su vez, soluciones para que se protejan. Así pues, el contenido de esta tesis ofrece diseño, análisis e implementación de metodologías que miden el impacto social y económico de la información personal online en los servicios extensivos de Internet. Principalmente, se centra en Facebook, una de las mayores redes sociales y servicios en la web, que cuenta con más de 2,8B de usuarios en todo el mundo y generó unos ingresos solo en publicidad online de más de 84 mil millones de dólares en el año 2020. En primer lugar, esta tesis presenta una solución, en forma de extensión del navegador llamada FDVT (Data Valuation Tool for Facebook users), para proporcionar a los usuarios una estimación personalizada y en tiempo real del dinero que están generando para Facebook. Analizando el número de anuncios e interacciones en una sesión, el usuario obtiene información sobre su valor dentro de esta red social. La extensión del navegador ha tenido una importante repercusión y adopción tanto por parte de los usuarios, instalándose más de 10k veces desde su lanzamiento público en octubre de 2016, como de los medios de comunicación, apareciendo en más de 100 medios. En segundo lugar, el estudio e investigación de los posibles riesgos asociados al tratamiento de los datos de los usuarios debe seguir también a la creación de este tipo de soluciones. En este contexto, esta tesis descubre y desvela resultados impactantes sobre el uso de la información personal: (i) cuantifica el número de usuarios afectados por el uso de atributos sensibles utilizados para la publicidad en Facebook, utilizando como referencia la definición de datos sensibles del Reglamento General de Protección de Datos. Esta tesis se basa en el uso de Procesamiento de Lenguaje Natural para identificar los atributos sensibles, y posteriormente utiliza el la plataforma de creación de anuncios de Facebook para recuperar el número de usuarios asignados con esta información sensible. Dos tercios de los usuarios de Facebook se ven afectados por el uso de datos personales sensibles que se les atribuyen. Además, la legislación parece no tener efecto en este uso de atributos sensibles por parte de Facebook, y presenta graves riesgos para los usuarios. (ii) Se modela cuál es el número de atributos que no identifican a priori personalmente al usuario y que aun así son suficientes para identificar de forma única a un individuo sobre una base de datos de miles de millones de usuarios, y se demuestra que llegar a un solo usuario es plausible incluso sin conocer datos que lo identifiquen personalmente de ellos mismos. Los resultados demuestran que 22 intereses al azar de un usuario son suficientes para identificarlo unívocamente con un 90% de probabilidad, y 4 si tomamos los menos populares. Por último, esta tesis se ha visto afectada por el estallido de la pandemia del COVID- 19, lo que ha contribuido al análisis de la evolución del mercado de la publicidad en línea con este periodo. La investigación demuestra que el mercado de la publicidad muestra una inelasticidad casi perfecta en la oferta y que cambió su composición debido a un cambio en el comportamiento en línea de los usuarios. También ilustra el potencial que tiene la utilización de los datos de los grandes servicios en línea, dado que ya tienen una alta tasa de adopción, y presenta un protocolo para la localización de contactos que han estado potencialmente expuestos a personas que direon positivo en COVID-19, en contraste con el fracaso de las nuevas aplicaciones de localización de contactos. En conclusión, la investigación de esta tesis muestra el impacto social y económico de la publicidad online y de los grandes servicios online en los usuarios. La metodología utilizada y desplegada sirve para poner de manifiesto y cuantificar los riesgos derivados de los datos personales en los servicios en línea. Presenta la necesidad de tales herramientas y metodologías en consonancia con la nueva legislación y los deseos de los usuarios. Siguiendo estas peticiones, en la búsqueda de transparencia y privacidad, esta tesis muestra soluciones y medidas fácilmente implementables para prevenir estos riesgos y capacitar al usuario para controlar su información personal.The web ecosystem is enormous, and overall it is sustained by an intangible attribute that mainly supports the majority of free services: the exploitation of personal information. Over the years, concerns on how services use personal data have increased and attracted the attention of media and users. This collection of personal information is the primary source of revenue on the Internet nowadays. Furthermore, on top of this, online advertising is the piece that supports it all. Without the existence of personal data in communion with online advertising, the Internet would probably not be the giant we know today. Online advertising is a very complex ecosystem in which multiple stakeholders take part. It is the motor that generates revenue on the web, and it has evolved in a few years to reach billions of users worldwide. While browsing, users generate valuable data about themselves that advertisers later use to offer them relevant products in which users could be interested. It is a two-way approach since advertisers pay intermediates to show ads to the public that is, in principle, most interested. However, this trading, sharing, and processing of personal data and behavior patterns, apart from opening up new advertising ways, expose users’ privacy. This incessant collection and commercialization of personal information usually fall behind an opaque wall, where the user often does not know what their data is used for. Privacy and transparency initiatives have increased over the years to empower the user in this business that moves billions of US dollars in revenue. Not surprisingly, after several scandals, such as the Facebook Cambridge Analytica scandal, businesses and regulators have joined forces to create transparency and protect users against the harmful practices derived from the use of their personal information. For instance, the General Data Protection Regulation (GDPR), is the most promising example of a data protection regulation, affecting all the member states of the European Union (EU), advocating for protecting users. The content of this thesis will use this legislation as a reference. For all these reasons, the purpose of this thesis is to provide tools and methodologies that reveal inappropriate uses of personal data by large companies in the online advertising ecosystem and create transparency among users, providing solutions to protect themselves. Thus, the content of this thesis offers design, analysis, and implementation of methodologies that measure online personal information’s social and economic impact on extensive Internet services. Mainly, it focuses on Facebook (FB), one of the largest social networks and services on the web, accounting with more than 2.8B Monthly Active Users (MAU) worldwide and generating only in online advertising revenue, more than $84B in 2020. First, this thesis presents a solution, in the form of a browser extension called Data Valuation Tool for Facebook users (FDVT), to provide users with a personalized, real-time estimation of the money they are generating for FB. By analyzing the number of ads and interactions in a session, the user gets information on their value within this social network. The add-on has had significant impact and adoption both by users, being installed more than 10k times since its public launch in October 2016, and media, appearing in more than 100 media outlets. Second, the study and research of the potential risks associated with processing users’ data should also follow the creation of these kinds of solutions. In this context, this thesis discovers and unveils striking results on the usage of personal information: (i) it quantifies the number of users affected by the usage of sensitive attributes used for advertising on FB, using as reference the definition of sensitive data from the GDPR. This thesis relies on the use of Natural Language Processing (NLP) to identify sensitive attributes, and it later uses the FB Ads Manager to retrieve the number of users assigned with this sensitive information. Two-thirds of FB users are affected by the use of sensitive personal data attributed to them. Moreover, the legislation seems not to affect this use of sensitive attributes from FB, and it presents severe risks to users. (ii) It models the number of non-Personal Identifiable Information (PII) attributes that are enough to uniquely identify an individual over a database of billions of users and proofs that reaching a single user is plausible even without knowing PII data of themselves. The results demonstrate that 22 interests at random from a user are enough to identify them uniquely with a 90% of probability, and 4 when taking the least popular ones. Finally, this thesis was affected by the outbreak of the COVID-19 pandemic what led to side contribute to the analysis of how the online advertising market evolved during this period. The research shows that the online advertising market shows an almost perfect inelasticity on supply and that it changed its composition due to a change in users’ online behavior. It also illustrates the potential of using data from large online services which already have a high adoption rate and presents a protocol for contact tracing individuals who have been potentially exposed to people who tested positive in COVID-19, in contrast to the failure of newly deployed contact tracing apps. In conclusion, the research for this thesis showcases the social and economic impact of online advertising and extensive online services on users. The methodology used and deployed is used to highlight and quantify the risks derived from personal data in online services. It presents the necessity of such tools and methodologies in line with new legislation and users’ desires. Following these requests, in the search for transparency and privacy, this thesis displays easy implementable solutions and measurements to prevent these risks and empower the user to control their personal information.This work was supported by the Ministerio de Educación, Cultura y Deporte, Spain, through the FPU Grant FPU16/05852, the Ministerio de Ciencia e Innovación, Spain, through the project ACHILLES Grant PID2019-104207RB-I00, the H2020 EU-Funded SMOOTH project under Grant 786741, and the H2020 EU-Funded PIMCITY project under Grant 871370.Programa de Doctorado en Ingeniería Telemática por la Universidad Carlos III de MadridPresidente: David Larrabeiti López.- Secretario: Gregorio Ignacio López López.- Vocal: Noel Cresp

Stumpage Appraisal Using Tender Price in Pahang, Peninsular Malaysia

Previous studies have shown that tropical forests are under valued. This may result in unsustainable harvest, biased against conservation and low revenue to the government. A study was conducted m the state of Pahang, Peninsula Malaysia to determine the trend and patterns of tender prices based on 277 logging transactions between the period 1993 to 1998. An analysis was also conducted to compare the actual tender price offered by the concessionaires with the estimated tender price using residual value technique for three logging compartments. Data on tender prices were collected from the Pahang State Forestry Department sales record. For the comparative analysis of tender price between actual and estimated price, data were obtained from state-own logging enterprises. These include data on timber volume, log price, logging and actual tender price. The result indicate that the average tender price offered by the concessionaires is RM6,271.44 per hectare, and the highest average tender price IS RM14,236.51 per hectare. The highest average tender prices (RM10,558.76 per hectare) is in the District of Lipis, followed by District of Jerantut (RM8,996.21). In general, the tender price has shown fluctuating trends during the study period, mainly due to economic situation and demand supply situation of log. The result from comparative analysis indicates that the concessionaires are relatively over-estimate the stumpage value, ranging from 6.4 to 20.14 percent. The study suggests that tendering would indicate full market value of a logging compartment when it is used for timber production. The estimated tender price using the residual value technique can be used to set the floor price. Further research is needed to obtain a comprehensive tendering mechanism before Implementing any police reform on forest revenue system in the state

Notes on some Dermestidae of British Columbia (Coleoptera)

N/