User's Privacy in Recommendation Systems Applying Online Social Network Data, A Survey and Taxonomy

Recommender systems have become an integral part of many social networks and extract knowledge from a user's personal and sensitive data both explicitly, with the user's knowledge, and implicitly. This trend has created major privacy concerns as users are mostly unaware of what data and how much data is being used and how securely it is used. In this context, several works have been done to address privacy concerns for usage in online social network data and by recommender systems. This paper surveys the main privacy concerns, measurements and privacy-preserving techniques used in large-scale online social networks and recommender systems. It is based on historical works on security, privacy-preserving, statistical modeling, and datasets to provide an overview of the technical difficulties and problems associated with privacy preserving in online social networks.Comment: 26 pages, IET book chapter on big data recommender system

Escaping the Big Brother: an empirical study on factors influencing identification and information leakage on the Web

This paper presents a study on factors that may increase the risks of personal information leakage, due to the possibility of connecting user profiles that are not explicitly linked together. First, we introduce a technique for user identification based on cross-site checking and linking of user attributes. Then, we describe the experimental evaluation of the identification technique both on a real setting and on an online sample, showing its accuracy to discover unknown personal data. Finally, we combine the results on the accuracy of identification with the results of a questionnaire completed by the same subjects who performed the test on the real setting. The aim of the study was to discover possible factors that make users vulnerable to this kind of techniques. We found out that the number of social networks used, their features and especially the amount of profiles abandoned and forgotten by the user are factors that increase the likelihood of identification and the privacy risks

Potential mass surveillance and privacy violations in proximity-based social applications

Proximity-based social applications let users interact with people that are currently close to them, by revealing some information about their preferences and whereabouts. This information is acquired through passive geo-localisation and used to build a sense of serendipitous discovery of people, places and interests. Unfortunately, while this class of applications opens different interactions possibilities for people in urban settings, obtaining access to certain identity information could lead a possible privacy attacker to identify and follow a user in their movements in a specific period of time. The same information shared through the platform could also help an attacker to link the victim's online profiles to physical identities. We analyse a set of popular dating application that shares users relative distances within a certain radius and show how, by using the information shared on these platforms, it is possible to formalise a multilateration attack, able to identify the user actual position. The same attack can also be used to follow a user in all their movements within a certain period of time, therefore identifying their habits and Points of Interest across the city. Furthermore we introduce a social attack which uses common Facebook likes to profile a person and finally identify their real identity

Modeling the Environmental and Socio-Economic Impacts of Biofuels

This paper provides a general overview of the social, environmental, and economical issues related to biofuels and a review of economic modeling of biofuels. The increasing importance of biofuels is driven primarily by government policies since currently available biofuels are generally not economically viable in the absence of fiscal incentives or high oil prices. Also the environmental impacts of biofuels as an alternative to fossil fuels are quite ambiguous. The literature review of the most recent economic models dealing with biofuels and their economic impacts provides a distinction between structural and reduced form models. The discussion of structural models centers primarily on computable general equilibrium models. The review of reduced models is structured toward the time series analysis approach to the dependencies between prices of biofuels, prices of agricultural commodities used for the biofuel production and prices of the fossil fuels.Biofuels; Ethanol; Biodiesel; Prices; Time-series

Urban Evolution: The Role of Water

The structure, function, and services of urban ecosystems evolve over time scales from seconds to centuries as Earth’s population grows, infrastructure ages, and sociopolitical values alter them. In order to systematically study changes over time, the concept of “urban evolution” was proposed. It allows urban planning, management, and restoration to move beyond reactive management to predictive management based on past observations of consistent patterns. Here, we define and review a glossary of core concepts for studying urban evolution, which includes the mechanisms of urban selective pressure and urban adaptation. Urban selective pressure is an environmental or societal driver contributing to urban adaptation. Urban adaptation is thesequential process by which an urban structure, function, or services becomes more fitted to its changing environment or human choices. The role of water is vital to driving urban evolution as demonstrated by historical changes in drainage, sewage flows, hydrologic pulses, and long-term chemistry. In the current paper, we show how hydrologic traits evolve across successive generations of urban ecosystems via shifts in selective pressures and adaptations over time. We explore multiple empirical examples including evolving: (1) urban drainage from stream burial to stormwater management; (2) sewage flows and water quality in response to wastewater treatment; (3) amplification of hydrologic pulses due to the interaction between urbanization and climate variability; and (4) salinization and alkalinization of fresh water due to human inputs and accelerated weathering. Finally, we propose a new conceptual model for the evolution of urban waters from the Industrial Revolution to the present day based on empirical trends and historical information. Ultimately, we propose that water itself is a critical driver of urban evolution that forces urban adaptation, which transforms the structure, function, and services of urban landscapes, waterways, and civilizations over time

From Data Flows to Privacy Issues: A User-Centric Semantic Model for Representing and Discovering Privacy Issues

In today\u27s highly connected cyber-physical world, people are constantly disclosing personal and sensitive data to different organizations and other people through the use of online and physical services. Such data disclosure activities can lead to unexpected privacy issues. However, there is a general lack of tools that help to improve users\u27 awareness of such privacy issues and to make more informed decisions on their data disclosure activities in wider contexts. To fill this gap, this paper presents a novel user-centric, data-flow graph based semantic model, which can show how a given user\u27s personal and sensitive data are disclosed to different entities and how different types of privacy issues can emerge from such data disclosure activities. The model enables both manual and automatic analysis of privacy issues, therefore laying the theoretical foundation of building data-driven and user-centric software tools for people to better manage their data disclosure activities in the cyber-physical world