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.
With new technology, people can share information about everyday places they go; the resulting data helps others find and evaluate places. Recent applications like Dodgeball and Sharescape repurpose everyday place information: users create local place data for personal use, and the systems display it for public use. We explore both the opportunities -- new local knowledge, and concerns -- privacy risks, raised by this implicit information sharing. We conduct two empirical studies: subjects create place data when using PlaceMail, a location-based reminder system, and elect whether to share it on Sharescape, a community map-building system. We contribute by: (1) showing location-based reminders yield new local knowledge about a variety of places, (2) identifying heuristics people use when deciding what place-related information to share (and their prevalence), (3) detailing how these decision heuristics can inform local knowledge sharing system design, and (4) identifying new uses of shared place information, notably opportunistic errand planning. Feedback is viewed as an essential element of ubiquitous computing systems in the HCI literature for helping people manage their privacy. However, the success of online social networks and existing commercial systems for mobile location sharing which do not incorporate feedback would seem to call the importance of feedback into question. We investigated this issue in the context of a mobile location sharing system. Specifically, we report on the findings of a field de-ployment of Locyoution, a mobile location sharing system. In our study of 56 users, one group was given feedback in the form of a history of location requests, and a second group was given no feedback at all. Our major contribution has been to show that feedback is an important contributing factor towards improving user comfort levels and allaying privacy concerns. Participants' privacy concerns were reduced after using the mobile location sharing system. Additionally,our study suggests that peer opinion and technical savviness contribute most to whether or not participants thought they would continue to use a mobile location technology. We report on a study (N=36) of user preferences for balancing awareness with privacy. Participants defined permissions for sharing of location, availability, calendar information and instant messaging (IM) activity within an application called mySpace. MySpace is an interactive visualization of the physical workplace that provides dynamic information about people, places and equipment. We found a significant preference for defining privacy permissions at the group level. While "family" received high levels of awareness sharing, interestingly, "team" was granted comparable levels during business hours at work. Surprisingly, presenting participants with a detailed list of all pieces of personal context to which the system had access, did not result in more conservative privacy settings. Although location was the most sensitive aspect of awareness, participants were comfortable disclosing room-level location information to their team members at work. Our findings suggest utilizing grouping mechanisms to balance privacy control with configuration burden, and argue for increased system transparency to build trust. Simulated, false location reports can be an effective way to confuse a privacy attacker. When a mobile user must transmit his or her location to a central server, these location reports can be accompanied by false reports that, ideally, cannot be distinguished from the true one. The realism of the false reports is important, because otherwise an attacker could filter out all but the real data. Using our database of GPS tracks from over 250 volunteer drivers, we developed probabilistic models of driving behavior and applied the models to create realistic driving trips. The simulations model realistic start and end points, slightly non-optimal routes, realistic driving speeds, and spatially varying GPS noise. This is a literature survey of computational location privacy, meaning computation-based privacy mechanisms that treat location data as geometric information. This definition includes privacy-preserving algorithms like anonymity and obfuscation as well as privacy-breaking algorithms that exploit the geometric nature of the data. The survey omits non-computational techniques like manually inspecting geotagged photos, and it omits techniques like encryption or access control that treat location data as general symbols. The paper reviews studies of peoples' attitudes about location privacy, computational threats on leaked location data, and computational countermeasures for mitigating these threats. We examine a very large-scale data set of more than 30 billion call records made by 25 million cell phone users across all 50 states of the US and attempt to determine to what extent anonymized location data can reveal private user information. Our approach is to infer, from the call records, the "top N" locations for each user and correlate this information with publicly-available side information such as census data. For example, the measured "top 2" locations likely correspond to home and work locations, the "top 3" to home, work, and shopping school commute path locations. We consider the cases where those "top N" locations are measured with different levels of granularity, ranging from a cell sector to whole cell, zip code, city, county and state. We then compute the anonymity set, namely the number of users uniquely identified by a given set of "top N" locations at different granularity levels. We find that the "top 1" location does not typically yield small anonymity sets. However, the top 2 and top 3 locations do, certainly at the sector or cell-level granularity. We consider a variety of different factors that might impact the size of the anonymity set, for example the distance between the "top N" locations or the geographic environment (rural vs urban). We also examine to what extent specific side information, in particular the size of the user's social network, decrease the anonymity set and therefore increase risks to privacy. Our study shows that sharing anonymized location data will likely lead to privacy risks and that, at a minimum, the data needs to be coarse in either the time domain (meaning the data is collected over short periods of time, in which case inferring the top N locations reliably is difficult) or the space domain (meaning the data granularity is strictly higher than the cell level). In both cases, the utility of the anonymized location data will be decreased, potentially by a significant amount. A significant and growing class of location-based mobile applications aggregate position data from individual devices at a server and compute aggregate statistics over these position streams. Because these devices can be linked to the movement of individuals, there is significant danger that the aggregate computation will violate the location privacy of individuals. This paper develops and evaluates PrivStats, a system for computing aggregate statistics over location data that simultaneously achieves two properties: first, provable guarantees on location privacy even in the face of any side information about users known to the server, and second, privacy-preserving accountability (i.e., protection against abusive clients uploading large amounts of spurious data). PrivStats achieves these properties using a new protocol for uploading and aggregating data anonymously as well as an efficient zero-knowledge proof of knowledge protocol we developed from scratch for accountability. We implemented our system on Nexus One smartphones and commodity servers. Our experimental results demonstrate that PrivStats is a practical system: computing a common aggregate (e.g., count) over the data of 10,000 clients takes less than 0.46 s at the server and the protocol has modest latency (0.6 s) to upload data from a Nexus phone. We also validated our protocols on real driver traces from the CarTel project. Advances in sensing and tracking technology enable location-based applications but they also create significant privacy risks. Anonymity can provide a high degree of privacy, save service users from dealing with service providers’ privacy policies, and reduce the service providers’ requirements for safeguarding private information. However, guaranteeing anonymous usage of location-based services requires that the precise location information transmitted by a user cannot be easily used to re-identify the subject. This paper presents a middleware architecture and algorithms that can be used by a centralized location broker service. The adaptive algorithms adjust the resolution of location information along spatial or temporal dimensions to meet specified anonymity constraints based on the entities who may be using location services within a given area. Using a model based on automotive traffic counts and cartographic material, we estimate the realistically expected spatial resolution for different anonymity constraints. The median resolution generated by our algorithms is 125 meters. Thus, anonymous location-based requests for urban areas would have the same accuracy currently needed for E-911 services; this would provide sufficient resolution for wayfinding, automated bus routing services and similar location-dependent services. Although the privacy threats and countermeasures associated with location data are well known, there has not been a thorough experiment to assess the effectiveness of either. We examine location data gathered from volunteer subjects to quantify how well four different algorithms can identify the subjects' home locations and then their identities using a freely available, programmable Web search engine. Our procedure can identify at least a small fraction of the subjects and a larger fraction of their home addresses. We then apply three different obscuration countermeasures designed to foil the privacy attacks: spatial cloaking, noise, and rounding. We show how much obscuration is necessary to maintain the privacy of all the subjects. Long-term personal GPS data is useful for many UbiComp services such as traffic monitoring and environmental impact assessment. However, inference attacks on such traces can reveal private information including home addresses and schedules. We asked 32 participants from 12 households to collect 2 months of GPS data, and showed it to them in visualizations. We explored if they understood how their individual privacy concerns mapped onto 5 location obfuscation schemes (which they largely did), which obfuscation schemes they were most comfortable with (Mixing, Deleting data near home, and Randomizing), how they monetarily valued their location data, and if they consented to share their data publicly. 21 32 gave consent to publish their data, though most households' members shared at different levels, which indicates a lack of awareness of privacy interrelationships. Grounded in real decisions about real data, our findings highlight the potential for end-user involvement in obfuscation of their own location data.