Sybil attacks against mobile users: friends and foes to the rescue

Collaborative applications for co-located mobile users can be severely disrupted by a sybil attack to the point of being unusable. Existing decentralized defences have largely been designed for peer-to-peer networks but not for mobile networks. That is why we propose a new decentralized defence for portable devices and call it MobID. The idea is that a device manages two small networks in which it stores information about the devices it meets: its network of friends contains honest devices, and its network of foes contains suspicious devices. By reasoning on these two networks, the device is then able to determine whether an unknown individual is carrying out a sybil attack or not. We evaluate the extent to which MobID reduces the number of interactions with sybil attackers and consequently enables collaborative applications.We do so using real mobility and social network data. We also assess computational and communication costs of MobID on mobile phones

Home Energy Efficiency and Mortgage Risks: Research funded by the Institute for Market Transformation

Many have theorized that energy efficient homes should have lower default risks than standard homes because the former are associated with lower energy costs, which leaves more money to make the mortgage payment. However, few empirical studies have been conducted due to limited data availability. This study examines actual loan performance data obtained from CoreLogic, the lending industry's leading source of such data. To assess whether residential energy efficiency is associated with lower default and prepayment risks, a national sample of about 71,000 ENERGY STAR- and non-ENERGY STAR-rated single-family home mortgages was carefully constructed, accounting for loan, household, and neighborhood characteristics.The study finds that default risks are on average 32 percent lower in energy-efficient homes, controlling for other loan determinants. This finding is robust, significant, and consistent across several model specifications. A borrower in an ENERGY STAR residence is also one-quarter less likely to prepay the mortgage. Within ENERGY STAR-rated homes, default risk is lower for more energy-efficient homes. The lower risks associated with energy efficiency should be taken into consideration when underwriting mortgages

StakeNet: using social networks to analyse the stakeholders of large-scale software projects

Many software projects fail because they overlook stakeholders or involve the wrong representatives of significant groups. Unfortunately, existing methods in stakeholder analysis are likely to omit stakeholders, and consider all stakeholders as equally influential. To identify and prioritise stakeholders, we have developed StakeNet, which consists of three main steps: identify stakeholders and ask them to recommend other stakeholders and stakeholder roles, build a social network whose nodes are stakeholders and links are recommendations, and prioritise stakeholders using a variety of social network measures. To evaluate StakeNet, we conducted one of the first empirical studies of requirements stakeholders on a software project for a 30,000-user system. Using the data collected from surveying and interviewing 68 stakeholders, we show that StakeNet identifies stakeholders and their roles with high recall, and accurately prioritises them. StakeNet uncovers a critical stakeholder role overlooked in the project, whose omission significantly impacted project success

TRULLO - local trust bootstrapping for ubiquitous devices

Handheld devices have become sufficiently powerful that it is easy to create, disseminate, and access digital content (e.g., photos, videos) using them. The volume of such content is growing rapidly and, from the perspective of each user, selecting relevant content is key. To this end, each user may run a trust model - a software agent that keeps track of who disseminates content that its user finds relevant. This agent does so by assigning an initial trust value to each producer for a specific category (context); then, whenever it receives new content, the agent rates the content and accordingly updates its trust value for the producer in the content category. However, a problem with such an approach is that, as the number of content categories increases, so does the number of trust values to be initially set. This paper focuses on how to effectively set initial trust values. The most sophisticated of the current solutions employ predefined context ontologies, using which initial trust in a given context is set based on that already held in similar contexts. However, universally accepted (and time invariant) ontologies are rarely found in practice. For this reason, we propose a mechanism called TRULLO (TRUst bootstrapping by Latently Lifting cOntext) that assigns initial trust values based only on local information (on the ratings of its user’s past experiences) and that, as such, does not rely on third-party recommendations. We evaluate the effectiveness of TRULLO by simulating its use in an informal antique market setting. We also evaluate the computational cost of a J2ME implementation of TRULLO on a mobile phone

Individual and Neighborhood Impacts of Neighborhood Reinvestment's Homeownership Pilot Program

The benefits of owning versus renting a home have been extolled by policy makers for many years, and there is substantial recent research to support those views. Yet the research supporting these claims largely has been conducted on general samples of homeowners. Low- and moderate-income homeowners may have a different experience due to difficulties in keeping up with housing-related payments or a difference in the quality of the homes being purchased. A major objective of this report is to assess the impacts of home ownership on a sample of low- and moderate-income homebuyers.We also know very little about the experience of lower-income homebuyers after they purchase their homes. To what extent do low-income homebuyers experience unexpected costs associated with maintenance or repairs? What proportion of low-income buyers take out home equity loans and what do they use the funds for? What proportion of low-income homebuyers default on their loans? What do buyers feel are the greatest advantages and challenges to owning a home? Answers to these questions may provide insight into how prospective lower-income homebuyers can be better prepared for home ownership.The research described in this report involved a sample of persons who graduated from home-ownership classes taught by eight NeighborWorks organizations that participated in the Neighborhood Reinvestment Homeownership Pilot program. Neighborhood Reinvestment has encouraged its affiliated NeighborWorks organizations to offer services designed to increase access to home ownership among low- and moderate-income families. Building on Neighborhood Reinvestment's Campaign for Home Ownership, the Homeownership Pilot program was designed to assist low- and moderate-income households to obtain home ownership by providing them with counseling, down-payment assistance and affordable loans.This report is the third of three reports on the implementation, outcomes and impacts of the Homeownership Pilot program. The first report, entitled An Assessment of Neighborhood Reinvestment's Homeownership Pilot Program: A Preliminary Report (2000), covered the early implementation of the Pilot. The second report, entitled Supporting the American Dream of Home Ownership: An Assessment of Neighborhood Reinvestment's Homeownership Pilot Program (2002), covers the outcomes of the Homeownership Pilot, including the number of persons counseled and new homebuyers assisted. This final report was designed to:1. Assess the proportion of customers trained by NeighborWorks organizations who go on to buy homes, as well as the factors that predict who among those graduating from the homeownership training go on to buy homes and who do not.2. Assess both the social and financial impacts of buying a home on the program participants.3. Assess the postpurchase experience of low-income homebuyers.4. Assess the loan repayment experience of a sample of the affordable loans held by Neighborhood Housing Services of America (NHSA).5. Assess changes in the Pilot program target areas before, during and after the Pilot program was in effect

StakeSource: harnessing the power of crowdsourcing and social networks in stakeholder analysis

Projects often fail because they overlook stakeholders. Unfortunately, existing stakeholder analysis tools only capture stakeholders' information, relying on experts to manually identify them. StakeSource is a web-based tool that automates stakeholder analysis. It "crowdsources" the stakeholders themselves for recommendations about other stakeholders and aggregates their answers using social network analysis

The Shortest Path to Happiness: Recommending Beautiful, Quiet, and Happy Routes in the City

When providing directions to a place, web and mobile mapping services are all able to suggest the shortest route. The goal of this work is to automatically suggest routes that are not only short but also emotionally pleasant. To quantify the extent to which urban locations are pleasant, we use data from a crowd-sourcing platform that shows two street scenes in London (out of hundreds), and a user votes on which one looks more beautiful, quiet, and happy. We consider votes from more than 3.3K individuals and translate them into quantitative measures of location perceptions. We arrange those locations into a graph upon which we learn pleasant routes. Based on a quantitative validation, we find that, compared to the shortest routes, the recommended ones add just a few extra walking minutes and are indeed perceived to be more beautiful, quiet, and happy. To test the generality of our approach, we consider Flickr metadata of more than 3.7M pictures in London and 1.3M in Boston, compute proxies for the crowdsourced beauty dimension (the one for which we have collected the most votes), and evaluate those proxies with 30 participants in London and 54 in Boston. These participants have not only rated our recommendations but have also carefully motivated their choices, providing insights for future work.Comment: 11 pages, 7 figures, Proceedings of ACM Hypertext 201

The Emotional and Chromatic Layers of Urban Smells

People are able to detect up to 1 trillion odors. Yet, city planning is concerned only with a few bad odors, mainly because odors are currently captured only through complaints made by urban dwellers. To capture both good and bad odors, we resort to a methodology that has been recently proposed and relies on tagging information of geo-referenced pictures. In doing so for the cities of London and Barcelona, this work makes three new contributions. We study 1) how the urban smellscape changes in time and space; 2) which emotions people share at places with specific smells; and 3) what is the color of a smell, if it exists. Without social media data, insights about those three aspects have been difficult to produce in the past, further delaying the creation of urban restorative experiences.Comment: 11 pages, 18 figures, final version published in the Proceedings of the Tenth International Conference on Web and Social Media (ICWSM 2016