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Full-Text Articles in Physical Sciences and Mathematics
Poster Abstract: Reliable People-Centric Sensing With Unreliable Voluntary Carriers, Cory Cornelius, Apu Kapadia, David Kotz, Dan Peebles, Minho Shin, Patrick Tsang
Poster Abstract: Reliable People-Centric Sensing With Unreliable Voluntary Carriers, Cory Cornelius, Apu Kapadia, David Kotz, Dan Peebles, Minho Shin, Patrick Tsang
Dartmouth Scholarship
As sensor technology becomes increasingly easy to integrate into personal devices such as mobile phones, clothing, and athletic equipment, there will be new applications involving opportunistic, people-centric sensing. These applications, which gather information about human activities and personal social context, raise many security and privacy challenges. In particular, data integrity is important for many applications, whether using traffic data for city planning or medical data for diagnosis. Although our AnonySense system (presented at MobiSys) addresses privacy in people-centric sensing, protecting data integrity in people-centric sensing still remains a challenge. Some mechanisms to protect privacy provide anonymity, and thus provide limited …
Anonysense: Opportunistic And Privacy-Preserving Context Collection, Apu Kapadia, Nikos Triandopoulos, Cory Cornelius, Dan Peebles, David Kotz
Anonysense: Opportunistic And Privacy-Preserving Context Collection, Apu Kapadia, Nikos Triandopoulos, Cory Cornelius, Dan Peebles, David Kotz
Dartmouth Scholarship
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' mobile phones to locate (Bluetooth-enabled) objects in their neighborhood. In most proposed applications, context reports include the time and location of the event, putting the privacy of users at increased risk—even if a report has been anonymized, the accompanying time and location can reveal sufficient information to deanonymize the user whose device sent the report. \par We propose AnonySense, a general-purpose architecture for leveraging users' mobile …