Location privacy preserving for semantic-aware applications

With the increase use of location-based services, location privacy has recently raised serious concerns. To protect a user from being identified, a cloaked spatial region that contains other k-1 nearest neighbors of the user is used to replace the accurate position. In this paper, we consider location-aware applications that services are different among regions. To search nearest neighbors, we define a novel distance measurement that combines the semantic distance and the Euclidean distance to address the privacy preserving issue in the above-mentioned applications. We also propose an algorithm kNNH to implement our proposed method. The experimental results further suggest that the proposed distance metric and the algorithm can successfully retain the utility of the location services while preserving users’ privacy.

Energy efficiency on location based applications in mobile cloud computing: a survey

The constrained battery power of mobile devices poses a serious impact on user experience. As an increasingly prevalent type of applications in mobile cloud environments, location-based applications (LBAs) present some inherent limitations concerning energy. For example, the Global Positioning System based positioning mechanism is well-known for its extremely power-hungry attribute. Due to the severity of the issue, considerable researches have focused on energy-efficient locating sensing mechanism in the last a few years. In this paper, we provide a comprehensive survey of recent work on low-power design of LBAs. An overview of LBAs and different locating sensing technologies used today are introduced. Methods for energy saving with existing locating technologies are investigated. Reductions of location updating queries and simplifications of trajectory data are also mentioned. Moreover, we discuss cloud-based schemes in detail which try to develop new energy-efficient locating technologies by leveraging the cloud capabilities of storage, computation and sharing. Finally, we conclude the survey and discuss the future research directions. © 2013 Springer-Verlag Wien.

A privacy-preserving location tracking system for smartphones based on cloud storage

With the widespread use of smartphones, the loss of a device is a critical problem, which results both in disrupting daily communications and losing valuable property. As a result, tracking systems have been developed to track mobile devices. Previous tracking systems focus on recovering the device's locations after it goes missing, with security methods implemented on the clients. However, users' locations are stored in untrusted third-party services, which may be attacked or eavesdropped. In this paper, we propose a system, named Android Cloud Tracker, to provide a privacy-preserving tracking client and safe storing of user's locations. We use cloud storage controlled by users themselves as storage facilities, and they do not need to worry about any untrusted third party. We implement Android Cloud Tracker prototype on Android phones, and the evaluation shows that it is both practical and lightweight: it generates a small amount of data flow and its distributed architecture provides strong guarantees of location privacy while preserving the ability to efficiently track missing devices.

The relative importance of spatial proximity, kin selection and potential ‘greenbeard’ signals on provisioning behaviour among helpers in a cooperative bird

© 2015 Springer-Verlag Berlin Heidelberg Many hypotheses have been proposed to account for cooperative behaviour, with those favouring kin selection receiving the greatest support to date. However, the importance of relatedness becomes less clear in complex societies where interactions can involve both kin and non-kin. To help clarify this, we examined the relative effect of indirect versus key direct benefit hypotheses in shaping cooperative decisions. We assessed the relative importance of likely reciprocal aid (as measured by spatial proximity between participants), kin selection (using molecular-based relatedness indices) and putative signals of relatedness (vocal similarity) on helper/helper cooperative provisioning dynamics in bell miners (Manorina melanophrys), a species living in large, complex societies. Using network analysis, we quantified the extent of shared provisioning (helping at the same nests) among individual helpers (excluding breeding pairs) over three seasons and 4290 provisioning visits, and compared these with the location of individuals within a colony and networks built using either genetic molecular relatedness or call similarity indices. Significant levels of clustering were observed in networks; individuals within a cluster were more closely related to each other than other colony members, and cluster membership was stable across years. The probability of a miner helping at another’s nest was not simply a product of spatial proximity and thus the potential for reciprocal aid. Networks constructed using helping data were significantly correlated to those built using molecular data in 5 of 10 comparisons, compared to 8 of 10 comparisons for networks constructed using call similarity. This suggests an important role of kinship in shaping helping dynamics in a complex cooperative society, apparently determined via an acoustic ‘greenbeard’ signal in this system.

A differentially private algorithm for location data release

The rise of mobile technologies in recent years has led to large volumes of location information, which are valuable resources for knowledge discovery such as travel patterns mining and traffic analysis. However, location dataset has been confronted with serious privacy concerns because adversaries may re-identify a user and his/her sensitivity information from these datasets with only a little background knowledge. Recently, several privacy-preserving techniques have been proposed to address the problem, but most of them lack a strict privacy notion and can hardly resist the number of possible attacks. This paper proposes a private release algorithm to randomize location dataset in a strict privacy notion, differential privacy, with the goal of preserving users’ identities and sensitive information. The algorithm aims to mask the exact locations of each user as well as the frequency that the user visits the locations with a given privacy budget. It includes three privacy-preserving operations: private location clustering shrinks the randomized domain and cluster weight perturbation hides the weights of locations, while private location selection hides the exact locations of a user. Theoretical analysis on privacy and utility confirms an improved trade-off between privacy and utility of released location data. Extensive experiments have been carried out on four real-world datasets, GeoLife, Flickr, Div400 and Instagram. The experimental results further suggest that this private release algorithm can successfully retain the utility of the datasets while preserving users’ privacy.

Continuous discovery of co-location contexts from Bluetooth data

The discovery of contexts is important for context-aware applications in pervasive computing. This is a challenging problem because of the stream nature of data, the complexity and changing nature of contexts. We propose a Bayesian nonparametric model for the detection of co-location contexts from Bluetooth signals. By using an Indian buffet process as the prior distribution, the model can discover the number of contexts automatically. We introduce a novel fixed-lag particle filter that processes data incrementally. This sampling scheme is especially suitable for pervasive computing as the computational requirements remain constant in spite of growing data. We examine our model on a synthetic dataset and two real world datasets. To verify the discovered contexts, we compare them to the communities detected by the Louvain method, showing a strong correlation between the results of the two methods. Fixed-lag particle filter is compared with Gibbs sampling in terms of the normalized factorization error that shows a close performance between the two inference methods. As fixed-lag particle filter processes a small chunk of data when it comes and does not need to be restarted, its execution time is significantly shorter than that of Gibbs sampling.

DoSTra: Discovering common behaviors of objects using the duration of staying on each location of trajectories

Since semantic trajectories can discover more semantic meanings of a user's interests without geographic restrictions, research on semantic trajectories has attracted a lot of attentions in recent years. Most existing work discover the similar behavior of moving objects through analysis of their semantic trajectory pattern, that is, sequences of locations. However, this kind of trajectories without considering the duration of staying on a location limits wild applications. For example, Tom and Anne have a common pattern of Home→Restaurant → Company → Restaurant, but they are not similar, since Tom works at Restaurant, sends snack to someone at Company and return to Restaurant while Anne has breakfast at Restaurant, works at Company and has lunch at Restaurant. If we consider duration of staying on each location we can easily to differentiate their behaviors. In this paper, we propose a novel approach for discovering common behaviors by considering the duration of staying on each location of trajectories (DoSTra). Our approach can be used to detect the group that has similar lifestyle, habit or behavior patterns and predict the future locations of moving objects. We evaluate the experiment based on synthetic dataset, which demonstrates the high effectiveness and efficiency of the proposed method.