Recent work has constructed economic mechanisms that are both truthful and differentially private. In these mechanisms, privacy is treated separately from truthfulness; it is not incorporated in players' utility functions (and doing so has been shown to lead to nontruthfulness in some cases). In this work, we propose a new, general way of modeling privacy in players' utility functions. Specifically, we only assume that if an outcome o has the property that any report of player i would have led to o with approximately the same probability, then o has a small privacy cost to player i. We give three mechanisms that are truthful with respect to our modeling of privacy: for an election between two candidates, for a discrete version of the facility location problem, and for a general social choice problem with discrete utilities (via a VCG-like mechanism). As the number n of players increases, the social welfare achieved by our mechanisms approaches optimal (as a fraction of n).
ABSTRACT:. By increasing the popularity of smart phones equipped with GPS sensors, more volunteers are expected to join VGI (Volunteered Geographic Information) activities and therefore more positional data will be collected in shorter time. Current statistics from open databases such OpenStreetMap reveals that although there have been exponential growth in the number of contributed POIs (Points of Interest), the lack of detailed attribute information is immediately visible. The process of adding attribute information to VGI databases is usually considered as a boring task and it is believed that contributors do not experience a similar level of satisfaction when they add such detailed information compared to tasks like adding new roads or copying building boundaries from satellite imageries. In other crowdsourcing projects, different approaches are taken for engaging contributors in problem solving by embedding the tasks inside a game. In the literature, this concept is known as "gamification" or "games with purpose" which encapsulate the idea of entertaining contributors while they are completing a particular defined task. Same concept is used to design a mobile application called "RoadPlex" which aims to collect general or specific attribute information for POIs The increased number of contributions in the past few months confirms that the design characteristics and the methodology of the game are appealing to players. Such growth enables us to evaluate the quality of the generated data through mining the database of answered questions. This paper reflects the some contribution results and emphasises the importance of using gamification concept in the domain of VGI.
ABSTRACT:In recent years, more and increased participation in Volunteered Geographical Information (VGI) projects provides enough data coverage for most places around the world for ordinary mapping and navigation purposes, however, the positional credibility of contributed data becomes more and more important to bring a long-term trust in VGI data. Today, it is hard to draw a definite traditional boundary between the authoritative map producers and the public map consumers and we observe that more and more volunteers are joining crowdsourcing activities for collecting geodata, which might result in higher rates of man-made mistakes in open map projects such as OpenStreetMap. While there are some methods for monitoring the accuracy and consistency of the created data, there is still a lack of advanced systems to automatically discover misplaced objects on the map. One feature type which is contributed daily to OSM is Point of Interest (POI). In order to understand how likely it is that a newly added POI represents a genuine real-world feature scientific means to calculate a probability of such a POI existing at that specific position is needed. This paper reports on a new analytic tool which dives into OSM data and finds co-existence patterns between one specific POI and its surrounding objects such as roads, parks and buildings. The platform uses a distance-based classification technique to find relationships among objects and tries to identify the high-frequency association patterns among each category of objects. Using such method, for each newly added POI, a probabilistic score would be generated, and the low scored POIs can be highlighted for editors for a manual check. The same scoring method can be used for existing registered POIs to check if they are located correctly. For a sample study, this paper reports on the evaluation of 800 pre-registered ATMs in Paris with associated scores to understand how outliers and fake entries could be detected automatically.
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