KeywordsElectrical & Computer Engineering
MetadataShow full item record
PublisherThe University of Arizona.
RightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
AbstractContinuous advances in electronics, wireless technologies, manufacturing processes, and software engineering have led to the proliferation of a plethora of mobile devices — mobile phones, tablets, wearables, sensors, smart consumer electronics, etc. — in our everyday lives. The interconnection of these devices into a single web of communication, information, and computation gives rise to a densely meshed wireless ecosystem that transforms the way users interact with their environment. However, ubiquitous interactions with devices that collect data about user activities pose challenging privacy and security problems. Without protection mechanisms, the systems we deploy breach user privacy, often without the user’s knowledge or consent. The collected information could reveal the user whereabouts, track his motion through space, infer his habits and personal preferences, record user relationships, acquaintances, and contacts, and compromise sensitive information. We investigate the leakage of the so-called contextual information in wireless communications. We focus on event-driven wireless sensor networks (WSNs), whereby wireless transmissions are triggered upon the detection of important events such as the detection of an object of interest, the recording of an abnormal physical parameter, etc. Privacy in event-driven WSNs is particularly important, because traffic patterns can be directly associated to events. We devise general traffic analysis techniques for extracting contextual information from WSN communications. We further investigate the inference of contextual information when the WSN transmissions are protected by traffic normalization methods, which rely on statistical source anonymity (SSA). To counter traffic analysis, we develop resource-efficient communication and routing methods for reporting events over multi-hop routes without revealing the event location and occurrence time, as well as the location of the sink. Our work explores the tradeoff between the communication overhead for normalizing traffic and the end-to-end real packet delay for delivering the event report to the sink. This is achieved by limiting the number of fake transmissions for obfuscating traffic patterns. To do so, we map the problem of selecting fake sources to the problem of finding a minimum connected dominating set (MCDS) that covers the WSN deployment area. We then impose transmission schedules on the fake sources to accelerate the delivery of real event reports. Finally, we propose strong privacy traffic normalization techniques that reduce the number of fake transmissions without relying on the concept of statistical source anonymity. In the proposed solution, the WSN is partitioned into connected dominating sets (CDSs) that are activated in a round-robin fashion. We show that our methods reduce the communication by several orders of magnitude, while maintaining privacy under strong adversary models.
Degree ProgramGraduate College
Electrical & Computer Engineering