From big smartphone data to worldwide research: The Mobile Data Challenge

Laurila, J.; Gática-Pérez, Daniel; Aad, Imad; Blom, Jan; Bornet, Olivier; Tri, Trinh Minh; Dousse, Olivier; Eberle, Julien; Miettinen, Markus

doi:10.1016/j.pmcj.2013.07.014

Cited by 100 publications

(71 citation statements)

References 23 publications

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“…Prior work has applied supervised learning to a small sample of smartphone sensor and usage data and achieved good performance at predicting marital status (37,53). {{,##,kk Related research has also demonstrated the feasibility of inferring relationship status from an online social network graph (38).…”

Section: Resultsmentioning

confidence: 99%

Evaluating the privacy properties of telephone metadata

Mayer

Mutchler

Mitchell

2016

Proc. Natl. Acad. Sci. U.S.A.

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Since 2013, a stream of disclosures has prompted reconsideration of surveillance law and policy. One of the most controversial principles, both in the United States and abroad, is that communications metadata receives substantially less protection than communications content. Several nations currently collect telephone metadata in bulk, including on their own citizens. In this paper, we attempt to shed light on the privacy properties of telephone metadata. Using a crowdsourcing methodology, we demonstrate that telephone metadata is densely interconnected, can trivially be reidentified, and can be used to draw sensitive inferences.C ommunications privacy law, in the United States and many other nations, draws a distinction between "content" and "metadata" (1). The former category reflects the substance of an electronic communication; the latter includes all other information about the communication, such as parties, time, and duration (2).* When a government agency compels disclosure of content, the agency must usually comply with extensive substantive and procedural safeguards. Demands for metadata, by contrast, are often left to the near-total discretion of authorities. In the United States, for instance, a law enforcement officer can request telephone calling records with merely a subpoena-essentially a formal letter from the investigating agency (3). An intelligence program by the National Security Agency (NSA) has drawn particular criticism; under the business records provision of the USA PATRIOT Act (4), the agency acquired a substantial share of all domestic telephone metadata (5). † In this paper, we empirically investigate factual assumptions that undergird policies of differential treatment for content and metadata. Using crowdsourced telephone logs and social networking information, we find that telephone metadata is densely interconnected, susceptible to reidentification, and enables highly sensitive inferences. ‡ The balance of the paper is organized into three parts. First, we discuss our data collection methodology and properties of our participant population. We next present our results. Finally, we discuss implications for policy and future quantitative social science research. Additional methodological detail and figures are available in the Supporting Information. MethodsWe collected the data in this study through an Android smartphone application ( Fig. 1). § Potential participants could discover the project through academic websites, the Google Play store, and references in media coverage. The application automatically retrieved historical call and text message [Short Message Service (SMS)] metadata from device logs. { In addition, the application retrieved information from a participant's Facebook account, to be used as ground truth for potential inferences. # Participants were provided an opportunity to view individualized features of their phone metadata, and then they were invited to uninstall the application. In total, 823 participants volunteered their metadata, which included 251,7...

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Section: Resultsmentioning

confidence: 99%

Evaluating the privacy properties of telephone metadata

Mayer

Mutchler

Mitchell

2016

Proc. Natl. Acad. Sci. U.S.A.

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“…Due to lack of freely available data sets, a highly convenient Android based app for tracking of users' behavior, environment and ground truth annotation was developed [Kiukkonen et al, 2010, Laurila et al, 2013, Yu Zheng, 2011. A sizeable amount of valid data was collected and submitted by 19 participants over a time span of 183 days.…”

Section: Resultsmentioning

confidence: 99%

“…Hence, the classification as either one of them lies solely in context of visit. Due to these circumstances the MDC treated these two place types as one [Laurila et al, 2013]. The relative high precision value of 0.867 suggests that distinguishing features exist, since nightlife is classified correctly, both place types can still be treated separately.…”

Section: Discussionmentioning

confidence: 99%

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Sensor Fusion for Semantic Place Labeling

Roor

Hess²,

Saveriano³

et al. 2017

Proceedings of the 3rd International Conference on Vehicle Technology and Intelligent Transport Systems

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“…The CloudAware has context adaptation feature to select appropriate connection selection and offload timing, by means of its history based prediction of user future movement, available c 2018 Information Processing Society of Japan bandwidth, and the execution time of an task. For realistic evaluation, they use Nokia Mobile Data Challenge (MDC) dataset [65] that contains activity records of data from smartphones of almost 200 volunteers over 18 months in Lausanne area, such as smartphone location, Cellular/WiFi connection status, device battery level, application usage, and so on. Evaluation is done in simulation in which each application tasks are judged either success or failure; the task may fail because of various reason such that the device battery runs out, the nearby MEC server is overloaded, or connection is lost by user's movement.…”

Section: Architectural Studiesmentioning

confidence: 99%

Multi-access Edge Computing: A Survey

Tanaka

Yoshida

Möri

et al. 2018

Journal of Information Processing

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Multi-access Edge Computing (MEC) can be defined as a model for enabling business oriented, cloud computing platform within multiple types of the access network (e.g., LTE, 5G, WiFi, FTTH, etc.) at the close proximity of subscribers to serve delay sensitive, context aware applications. To pull out the most of the potential, MEC has to be designed as infrastructure, to support many kind of IoT applications and their eco system, in addition to sufficiently management mechanism. In this context, various research and standardization efforts are ongoing. This paper provides a comprehensive survey of the state-of-the-art research efforts on MEC domain, with focus on the architectural proposals as infrastracture, the issue of the partitioning of processing among the user devices, edge servers, and a cloud, and the issue of the resource management.

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From big smartphone data to worldwide research: The Mobile Data Challenge

Cited by 100 publications

References 23 publications

Evaluating the privacy properties of telephone metadata

Evaluating the privacy properties of telephone metadata

Sensor Fusion for Semantic Place Labeling

Multi-access Edge Computing: A Survey

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