1,217 research outputs found

    Location Privacy for Mobile Crowd Sensing through Population Mapping

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    Opportunistic sensing allows applications to “task” mobile devices to measure context in a target region. For example, one could leverage sensor-equipped vehicles to measure traffic or pollution levels on a particular street or users\u27 mobile phones to locate (Bluetooth-enabled) objects in their vicinity. In most proposed applications, context reports include the time and location of the event, putting the privacy of users at increased risk: even if identifying information has been removed from a report, the accompanying time and location can reveal sufficient information to de-anonymize the user whose device sent the report. We propose and evaluate a novel spatiotemporal blurring mechanism based on tessellation and clustering to protect users\u27 privacy against the system while reporting context. Our technique employs a notion of probabilistic k-anonymity; it allows users to perform local blurring of reports efficiently without an online anonymization server before the data are sent to the system. The proposed scheme can control the degree of certainty in location privacy and the quality of reports through a system parameter. We outline the architecture and security properties of our approach and evaluate our tessellation and clustering algorithm against real mobility traces

    A Taxonomy for and Analysis of Anonymous Communications Networks

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    Any entity operating in cyberspace is susceptible to debilitating attacks. With cyber attacks intended to gather intelligence and disrupt communications rapidly replacing the threat of conventional and nuclear attacks, a new age of warfare is at hand. In 2003, the United States acknowledged that the speed and anonymity of cyber attacks makes distinguishing among the actions of terrorists, criminals, and nation states difficult. Even President Obama’s Cybersecurity Chief-elect recognizes the challenge of increasingly sophisticated cyber attacks. Now through April 2009, the White House is reviewing federal cyber initiatives to protect US citizen privacy rights. Indeed, the rising quantity and ubiquity of new surveillance technologies in cyberspace enables instant, undetectable, and unsolicited information collection about entities. Hence, anonymity and privacy are becoming increasingly important issues. Anonymization enables entities to protect their data and systems from a diverse set of cyber attacks and preserves privacy. This research provides a systematic analysis of anonymity degradation, preservation and elimination in cyberspace to enhance the security of information assets. This includes discovery/obfuscation of identities and actions of/from potential adversaries. First, novel taxonomies are developed for classifying and comparing well-established anonymous networking protocols. These expand the classical definition of anonymity and capture the peer-to-peer and mobile ad hoc anonymous protocol family relationships. Second, a unique synthesis of state-of-the-art anonymity metrics is provided. This significantly aids an entity’s ability to reliably measure changing anonymity levels; thereby, increasing their ability to defend against cyber attacks. Finally, a novel epistemic-based mathematical model is created to characterize how an adversary reasons with knowledge to degrade anonymity. This offers multiple anonymity property representations and well-defined logical proofs to ensure the accuracy and correctness of current and future anonymous network protocol design

    Privacy in trajectory micro-data publishing : a survey

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    We survey the literature on the privacy of trajectory micro-data, i.e., spatiotemporal information about the mobility of individuals, whose collection is becoming increasingly simple and frequent thanks to emerging information and communication technologies. The focus of our review is on privacy-preserving data publishing (PPDP), i.e., the publication of databases of trajectory micro-data that preserve the privacy of the monitored individuals. We classify and present the literature of attacks against trajectory micro-data, as well as solutions proposed to date for protecting databases from such attacks. This paper serves as an introductory reading on a critical subject in an era of growing awareness about privacy risks connected to digital services, and provides insights into open problems and future directions for research.Comment: Accepted for publication at Transactions for Data Privac
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