Today people increasingly have the opportunity to opt-in to "usage-based" automotive insurance programs for reducing insurance premiums. In these programs, participants install devices in their vehicles that monitor their driving behavior, which raises some privacy concerns. Some devices collect fine-grained speed data to monitor driving habits. Companies that use these devices claim that their approach is privacy-preserving because speedometer measurements do not have physical locations. However, we show that with knowledge of the user's home location, as the insurance companies have, speed data is sufficient to discover driving routes and destinations when trip data is collected over a period of weeks. To demonstrate the real-world applicability of our approach we applied our algorithm, elastic pathing, to data collected over hundreds of driving trips occurring over several months. With this data and our approach, we were able to predict trip destinations to within 250 meters of ground truth in 10 of the traces and within 500 meters in 20 of the traces. This result, combined with the amount of speed data that is being collected by insurance companies, constitutes a substantial breach of privacy because a person's regular driving pattern can be deduced with repeated examples of the same paths with just a few weeks of monitoring.
Ubiquitous computing devices help people during their daily lives in different contexts, for instance by monitoring our activity and fitness levels or interesting events social that we have attended. However, the ubiquitous nature of these monitoring devices has security and privacy ramifications that are not always considered. For example, a lack of sufficient protection in the Nike+iPod sport kit allowed other people to wirelessly monitor and track users during their daily activities @cite_4 . Although these kinds of devices clearly appeal to consumers, the potential loss of privacy is a growing concern.
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Ubicomp researchers have long argued that privacy is a design issue, and it goes without saying that successful design requires that we understand the desires, concerns, and awareness of the technology's users. Yet, because ubicomp systems are relatively unusual, too little empirical research exists to inform designers about potential users. Complicating design further is the fact that ubicomp systems are typically embedded or invisible, making it difficult for users to know when invisible devices are present and functioning. As early as 1993, ubicomp researchers recognized that embedded technology's unobtrusiveness both belies and contributes to its potential for supporting potentially invasive applications. Not surprisingly, users' inability to see a technology makes it difficult for them to understand how it might affect their privacy. Unobtrusiveness, nevertheless, is a reasonable goal because such systems must minimize the demands on users. To investigate these issues further, I conducted an ethnographic study of what I believe is the first US eldercare facility to use a sensor-rich environment. Our subjects were normal civilians (rather than ubicomp researchers) who lived or worked in a ubiquitous computing environment. We interviewed residents, their family members, and the facility's caregivers and managers. Our questions focused on how people understood both the ubiquitous technology and its effect on their privacy. Although the embedded technology played a central role in how people viewed the environment, they had a limited understanding of the technology, thus raising several privacy, design, and safety issues. As the proliferation of pervasive and ubiquitous computing devices continues, users will carry more devices. Without the ability for these devices to unobtrusively interact with one another, the user’s attention will be spent on coordinating, rather than using, these devices. We present a method to determine if two devices are carried by the same person, by analyzing walking data recorded by low-cost MEMS accelerometers using the coherence function, a measure of linear correlation in the frequency domain. We also show that these low-cost sensors perform similarly to more expensive accelerometers for the frequency range of human motion, 0 to 10Hz. We also present results from a large test group illustrating the algorithm’s robustness and its ability to withstand real world time delays, crucial for wireless technologies like Bluetooth and 802.11. We present results that show that our technique is 100 accurate using a sliding window of 8 seconds of data when the devices are carried in the same location on the body, is tolerant to inter-device communication latencies, and requires little communication bandwidth. In addition we present results for when devices are carried on different parts of the body. Ubiquitous computing is associated with a vision of everything being connected to everything. However, for successful applications to emerge, it will not be the quantity but the quality and usefulness of connections that will matter. Our concern is how qualitative relations and more selective connections can be established between smart artefacts, and how users can retain control over artefact interconnection. We propose context proximity for selective artefact communication, using the context of artefacts for matchmaking. We further suggest to empower users with simple but effective means to impose the same context on a number of artefacts. To prove our point we have implemented Smart-Its Friends, small embedded devices that become connected when a user holds them together and shakes them. Accurate recognition and tracking of human activities is an important goal of ubiquitous computing. Recent advances in the development of multi-modal wearable sensors enable us to gather rich datasets of human activities. However, the problem of automatically identifying the most useful features for modeling such activities remains largely unsolved. In this paper we present a hybrid approach to recognizing activities, which combines boosting to discriminatively select useful features and learn an ensemble of static classifiers to recognize different activities, with hidden Markov models (HMMs) to capture the temporal regularities and smoothness of activities. We tested the activity recognition system using over 12 hours of wearable-sensor data collected by volunteers in natural unconstrained environments. The models succeeded in identifying a small set of maximally informative features, and were able identify ten different human activities with an accuracy of 95 .