Camouflaging data by generating fake information is a well-known obfuscation technique for protecting data privacy. In this paper, we focus on a very sensitive and increasingly exposed type of data: location data. There are two main scenarios in which fake traces are of extreme value to preserve location privacy: publishing datasets of location trajectories, and using location-based services. Despite advances in protecting (location) data privacy, there is no quantitative method to evaluate how realistic a synthetic trace is, and how much utility and privacy it provides in each scenario. Also, the lack of a methodology to generate privacy-preserving fake traces is evident. In this paper, we fill this gap and propose the first statistical metric and model to generate fake location traces such that both the utility of data and the privacy of users are preserved. We build upon the fact that, although geographically they visit distinct locations, people have strongly semantically similar mobility patterns, for example, their transition pattern across activities (e.g., working, driving, staying at home) is similar. We define a statistical metric and propose an algorithm that automatically discovers the hidden semantic similarities between locations from a bag of real location traces as seeds, without requiring any initial semantic annotations. We guarantee that fake traces are geographically dissimilar to their seeds, so they do not leak sensitive location information. We also protect contributors to seed traces against membership attacks. Interleaving fake traces with mobile users' traces is a prominent location privacy defense mechanism. We quantitatively show the effectiveness of our methodology in protecting against localization inference attacks while preserving utility of sharing publishing traces.
Hiding the user's true location among fake locations is a promising yet very little-explored approach to protecting location privacy. There are few simple techniques proposed so far: adding independently selected fake locations drawn from the population's location distribution @cite_22 , generating dummy locations at random as a random walk on a grid @cite_14 @cite_23 , constructing fake driving trips by building the path between two random locations on the map given the more probable paths traveled by drivers @cite_27 , or adding noise to the paths generated by road trip planner algorithms @cite_18 . These solutions lack a formal model for human mobility and do not consider the semantics associated with sequence of locations visited by people over time. Thus, the generated traces can be distinguished from real location traces.
@cite_18 @cite_14 @cite_22 @cite_27 @cite_23
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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. Due to the popularization of location based services (LBSs), preserving user's location privacy has become a significant issue. In this paper, we assume that users are traveling by car and propose a location anonymization method which mainly aims at reducing traceability of user's movement trajectory. Since the movement patterns of car-driving users have some special characteristics due to various factors such as traffic rules, generating dummies in an artificially-synthesized manner is not practical. Therefore, in our method, we use real car trace data which were obtained from probe cars to generate dummies so that the generated dummies look like real users. In doing so, our method tries to find real traces which can cross with the user or another dummy at an intersection in order to reduce the user's traceability. Through simulations, we confirm that our method can reduce the traceability of the user's trajectory compared with some naive approaches. This paper presents a privacy-preserving framework for the protection of sensitive positions in real time trajectories. We assume a scenario in which the sensitivity of user's positions is space-varying, and so depends on the spatial context, while the user's movement is confined to road networks and places. Typical users are the non-anonymous members of a geo-social network who agree to share their exact position whenever such position does not fall within a sensitive place, e.g. a hospital. Suspending location sharing while the user is inside a sensitive place is not an appropriate solution because the user's stopovers can be easily inferred from the user's trace. In this paper we present an extension of the semantic location cloaking model [1] originally developed for the cloaking of non-correlated positions in an unconstrained space. We investigate different algorithms for the generation of cloaked regions over the graph representing the urban setting. We also integrate methods to prevent velocity based linkage attacks. Finally we evaluate experimentally the algorithms using a real data set. Location is a key context ingredient and many existing pervasive applications rely on the current locations of their users. However, with the ability to predict the future location and movement behavior of a user, the usability of these applications can be greatly improved. In this paper, we propose an approach to predict both the intended destination and the future route of a person. Rather than predicting the destination and future route separately, we have focused on making prediction in an integrated way by exploiting personal movement data (i.e. trajectories) collected by GPS. Since trajectories contain daily whereabouts information of a person, the proposed approach first detects the significant places where the person may depart from or go to using a clustering-based algorithm called FBM (Forward-Backward Matching), then abstracts the trajectories based on a space partitioning method, and finally extracts movement patterns from the abstracted trajectories using an extended CRPM (Continuous Route Pattern Mining) algorithm. Extracted movement patterns are organized in terms of origin-destination couples. The prediction is made based on a pattern tree built from these movement patterns. With the real personal movement data of 14 participants, we conducted a number of experiments to evaluate the performance of our system. The results show that our approach can achieve approximately 80 and 60 accuracy in destination prediction and 1-step prediction, respectively, and result in an average deviation of approximately 60 m in continuous future route prediction. Finally, based on the proposed approach, we implemented a prototype running on mobile phones, which can extract patterns from a user's historical movement data and predict the destination and future route.