1,686 research outputs found

    Location Privacy in the Era of Big Data and Machine Learning

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    Location data of individuals is one of the most sensitive sources of information that once revealed to ill-intended individuals or service providers, can cause severe privacy concerns. In this thesis, we aim at preserving the privacy of users in telecommunication networks against untrusted service providers as well as improving their privacy in the publication of location datasets. For improving the location privacy of users in telecommunication networks, we consider the movement of users in trajectories and investigate the threats that the query history may pose on location privacy. We develop an attack model based on the Viterbi algorithm termed as Viterbi attack, which represents a realistic privacy threat in trajectories. Next, we propose a metric called transition entropy that helps to evaluate the performance of dummy generation algorithms, followed by developing a robust dummy generation algorithm that can defend users against the Viterbi attack. We compare and evaluate our proposed algorithm and metric on a publicly available dataset published by Microsoft, i.e., Geolife dataset. For privacy preserving data publishing, an enhanced framework for anonymization of spatio-temporal trajectory datasets termed the machine learning based anonymization (MLA) is proposed. The framework consists of a robust alignment technique and a machine learning approach for clustering datasets. The framework and all the proposed algorithms are applied to the Geolife dataset, which includes GPS logs of over 180 users in Beijing, China

    Who Wants to Revise Privatization and Why? Evidence from 28 Post-Communist Countries

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    A 2006 survey of 28,000 individuals in 28 post-communist countries reveals overwhelming public support for the revision of privatization in the region. A majority of respondents, however, favors a revision of privatization that ultimately leaves firms in private hands. We identify which factors influence individuals’ support for revising privatization and explore whether respondents’ views are driven by a preference for state property or a concern for the fairness of privatization. We find that human capital poorly suited for a market economy with private ownership and a lack of privately owned assets increase support for revising privatization with the primary reason being a preference for state over private property. Economic hardships during transition and work in the state sector also increase support for revising privatization, but mainly due to the perceived unfairness of privatization. The effects of human capital and asset ownership on support for revising privatization are independent of a countries’ institutional environment. In contrast, good governance institutions amplify the impact of positive and negative transition experiences on attitudes toward revising privatization. In countries with low inequality,individuals with positive and negative transition experiences hold significantly different views about the superiority of private property, but this difference is much smaller in countries with high inequality.privatization, revision, nationalization, property rights, demand for property rights, legitimacy of property rights, transition

    Exploring historical location data for anonymity preservation in location-based services

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    We present a new approach for K-anonymity protection in Location-Based Services (LBSs). Specifically, we depersonalize location information by ensuring that each location reported for LBSs is a cloaking area that contains K different footprints--- historical locations of different mobile nodes. Therefore, the exact identity and location of the service requestor remain anonymous from LBS service providers. Existing techniques, on the other hand, compute the cloaking area using current locations of K neighboring hosts of the service requestor. Because of this difference, our approach significantly reduces the cloaking area, which in turn decreases query processing and communication overhead for returning query results to the requesting host. In addition, existing techniques also require frequent location updates from all nodes, regardless of whether or not these nodes are requesting LBSs. Most importantly, our approach is the first practical solution that provides K-anonymity trajectory protection needed to ensure anonymity when the mobile host requests LBSs continuously as it moves. Our solution depersonalizes a user\u27s trajectory (a time-series of the user\u27s locations) based on the historical trajectories of other users

    Location Privacy and Its Applications: A Systematic Study

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    © 2013 IEEE. This paper surveys the current research status of location privacy issues in mobile applications. The survey spans five aspects of study: the definition of location privacy, attacks and adversaries, mechanisms to preserve the privacy of locations, location privacy metrics, and the current status of location-based applications. Through this comprehensive review, all the interrelated aspects of location privacy are integrated into a unified framework. Additionally, the current research progress in each area is reviewed individually, and the links between existing academic research and its practical applications are identified. This in-depth analysis of the current state-of-play in location privacy is designed to provide a solid foundation for future studies in the field

    Privacy preservation in mobile social networks

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    In this day and age with the prevalence of smartphones, networking has evolved in an intricate and complex way. With the help of a technology-driven society, the term "social networking" was created and came to mean using media platforms such as Myspace, Facebook, and Twitter to connect and interact with friends, family, or even complete strangers. Websites are created and put online each day, with many of them possessing hidden threats that the average person does not think about. A key feature that was created for vast amount of utility was the use of location-based services, where many websites inform their users that the website will be using the users' locations to enhance the functionality. However, still far too many websites do not inform their users that they may be tracked, or to what degree. In a similar juxtaposed scenario, the evolution of these social networks has allowed countless people to share photos with others online. While this seems harmless at face-value, there may be times in which people share photos of friends or other non-consenting individuals who do not want that picture viewable to anyone at the photo owner's control. There exists a lack of privacy controls for users to precisely de fine how they wish websites to use their location information, and for how others may share images of them online. This dissertation introduces two models that help mitigate these privacy concerns for social network users. MoveWithMe is an Android and iOS application which creates decoys that move locations along with the user in a consistent and semantically secure way. REMIND is the second model that performs rich probability calculations to determine which friends in a social network may pose a risk for privacy breaches when sharing images. Both models have undergone extensive testing to demonstrate their effectiveness and efficiency.Includes bibliographical reference

    Powerful Multinational or Persecuted Foreigners: ‘Foreignness’ and Influence over Government

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    One of the enduring themes of the globalization debate is whether international law should be strengthened to protect foreign firm from discriminatory host governments, or rather strengthened to protect host governments from powerful multinational firms. This paper uses firm-level data from the World Business Environment Survey (WBES) to lend some empirical evidence to the debate. In doing so it contributes to academic understanding of what a `foreign firm' is, and challenges the notion that institutional superiority makes OECD governments less prone to anti-foreign bias. Although the terms `foreign firm' and `multinational subsidiary' are often used interchangeably, in the WBES data the managers of only about half of the firms with more than ten percent foreign ownership view themselves as part of a multinational. This distinction between multinational and non-multinational foreign firms was important in regression analysis of self-reported influence over government. In non- OECD countries - where we find no evidence of anti-foreign bias - multinationals appear significantly more influential than other firms. Meanwhile, in OECD countries, foreign non-multinationals do appear at a disadvantage in terms of influence relative to domestic firms, but this `liability of foreignness' does not appear to extend to foreign-multinational affiliates.Multinational Firms, Foreign Firms, Political Economy, Government
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