Landscape-aware location-privacy protection in location-based services

Influence maximization is one of the most fundamental problems in social network analysis due to its significant impact on viral marketing and targeted advertisements. Considerable amount of works has extensively studied to analyze the influence in social networks, but most of the existing works unfortunately neglected the fact that the location information in the cyber-physical world could also play an important role in the influence prorogation. Furthermore, even though a few works consider a little location information to enhance the influence maximization, they do not discuss any privacy issue of the model and expose user's location information directly to the public. This paper considers the problem of influence maximization in both GPS-enabled cyber-physical and online social networks with privacy reservation. We propose one novel model merging both GPS data of cyber-physical network and relationship data of online social network together in a unified framework, then we provide an efficient algorithm to solve the influence maximization problem in the framework. Besides the influence maximization problem, our framework could also support other applications involving both cyber-physical and online social networks. Further more, to protect the sensitive location and link information, we also provide corresponding techniques to protect the privacy during the whole influence maximization process. Empirical studies of real life datasets demonstrate the power of engaging location information to influence maximization, and suggest that our resolution outperforming most existing alternative algorithms with the protection of location privacy.

The ubiquity of mobile devices with global positioning functionality (e.g., GPS and Assisted GPS) and Internet connectivity (e.g., 3G and Wi-Fi) has resulted in widespread development of location-based services (LBS). Typical examples of LBS include local business search, e-marketing, social networking, and automotive traffic monitoring. Although LBS provide valuable services for mobile users, revealing their private locations to potentially untrusted LBS service providers pose privacy concerns. In general, there are two types of LBS, namely, snapshot and continuous LBS. For snapshot LBS, a mobile user only needs to report its current location to a service provider once to get its desired information. On the other hand, a mobile user has to report its location to a service provider in a periodic or on-demand manner to obtain its desired continuous LBS. Protecting user location privacy for continuous LBS is more challenging than snapshot LBS because adversaries may use the spatial and temporal correlations in the user's a sequence of location samples to infer the user's location information with a higher degree of certainty. Such user spatial trajectories are also very important for many applications, e.g., business analysis, city planning, and intelligent transportation. However, publishing original spatial trajectories to the public or a third party for data analysis could pose serious privacy concerns. Privacy protection in continuous LBS and trajectory data publication has increasingly drawn attention from the research community and industry. In this chapter, we describe the state-of-the-art privacy-preserving techniques for continuous LBS and trajectory publication.

Recent mobile devices are mostly equipped with a GPS receiver. This trend has arisen a variety of location based services (LBSs) which enable users to search local information of their current locations. To use LBSs, a user needs to send his/her location information to a service provider. Users' location information is inherently private, since it can reveal the critical information. In this paper, we propose a method to protect the user's location privacy by sending the user's location with dummy locations, which are determined based on the user's current location. We conduct an experiment to evaluate the effectiveness of our proposed method using real users' trajectories on a real map.

With the continuous development of mobile internet, location-based services are widely used in our daily life. The issue of mobile user privacy disclosure is unavoidable. Attackers can acquire sensitive information such as user identity, privacy and so on, according to the user's location information. To solve the issue, this paper proposed an improved third-party anonymous server framework to protect the mobile user location for KNN (K-nearest neighbor) query. The framework prevents a third-party anonymous server from being attacked and thus compromise of user information. Meanwhile, it allocates part of the computation to mobile devices in order to reduce the possibility of computing performance constraints from the servers. On this basis, a new client POI (point of interest) search algorithm DATwist is put forward, which searches the POIs based on double anchors points. The DATwist algorithm can fix the defects that the POIs are distributed around the anchor points and the search results are unbalanced in the SpaceTwist algorithm, enabling a more accurate and effective KNN progress. Finally, the comparing experiments proved that the DATwist algorithm has more advantages than the HINN (homogeneous incremental nearest neighbor) algorithm in effectiveness and communication overhead with uniform distribution of POIs query results.

As use of location-based services (LBSs) is becoming increasingly prevalent, mobile users are more and more enticed to reveal their locations, which may be exploited by attackers to infer the points of interest (POIs) the users visit and then their privacy information. We propose a novel approach to the protection of a user's location privacy based on unobservability, preventing the attackers from relating any particular POI to the user's current location. We design, implement, and evaluate a privacy-protection system, called the Location Information ScrAmbler (LISA) which protects the user's location privacy by adjusting the location noise and hence, the uncertainty of associating his location with any POI, while conserving resources (especially battery energy) on mobile devices. By protecting location privacy locally on each mobile user's device, LISA eliminates the reliance on the trusted third-party servers required by most existing approaches. Therefore, it not only avoids the vulnerability of a single point of failure, but also facilitates the deployment of LBSs. Our evaluation of LISA using real-world users' traces demonstrates its efficacy and efficiency.

In recent years, with the spread of social networking services (hereinafter referred to as SNS), information on events of various sizes, including local events and ongoing events, has been posted on SNS. Research has already begun to create systems that recommend events for users to attend at specific times and places based on a collection of event information and using user location data. However, for the user, there is a concern regarding privacy that the user's location information or the “like” reaction within SNS to certain events is transmitted to the server as is and used for information analysis. Therefore, in this work, we considered the user's privacy by converting the user's position information into a dummy position using regional mesh information and utilizing it for the inquiry to the server. We examined a method of acquiring event information from Twitter, which is a type of SNS, and searching for event information from a large amount of SNS data after satisfying the geographical constraints on the user side.

The widespread use of location-based services (LBS), in which any informative service is provided simply based on the user's present location, has generated serious concerns about the user's location privacy. For example, if a customer wants to know “where is the nearest ATM machine?”, she must provide her exact geographical coordinates in order to receive the location-based informative services she requested. Although location-based services open up a wide range of markets and give remarkable convenience to the end user, they also bring minor privacy threats to the user's location data. The requirement that the user informs the LBS provider of their current location in order to get connected services exposes the system to a risk of privacy invasion. Because the volume of data collected from moving or stationary mobile users utilizing LBS may be large, it is critical to design safe frameworks and systems that keep location information private. The two most common techniques to preserving location privacy in LBS are cloaking and obfuscation. These solutions leverage a trusted third party (TTP) and the K-anonymity principle to make the query issuer indistinguishable from other K 1 additional users. The thesis's initial goal is to investigate the user privacy concerns raised by location-based services, and it presents the VIC-PRO scheme, a TTP-based technique to avoid location-based proximity inference of the user who submits a query. The proposed VIC-PRO technique enhances the privacy of query initiating proximity information and helps to close the holes in the present system's K-anonymity approach. In TTP-based methods, all data (namely location coordinates and query) is available at the central server; hence, the central node has complete awareness of the query (including user ID). This is a fundamental flaw in TTP-based design, and it renders these frameworks vulnerable to various privacy assaults. We observe the need for collaboration-based communication between peer users who belong to the population of mobile users in a decentralized or TTP-free architecture and propose CAST (acronym for CAching with truST), a collaborative P2P communication model employing a cascading series of trust between peers, where peers use cached data to collaborate with one another and the results can be obtained. The method operates effectively and gives results locally with little latency when the peers have similar interests (or data value). Under a pull-based sporadic query situation, the suggested algorithm prioritizes user privacy and performs well. We utilize the benefits and mitigate the flaws of both traditional current techniques since measuring and evaluating peer trust have always been a vital aspect. We offer HYB, a hybrid approach, for achieving location privacy for mobile users that regularly utilize location services. The proposed HYB method is based on collaborative location data preprocessing and makes use of homomorphic encryption techniques. Location privacy may be accomplished on two levels: close proximity and far distance. Under a particular, pull-based, occasional query situation, the suggested privacy algorithms successfully safeguard user location privacy. The privacy issue becomes critical when location-tagged data publications, such as public healthcare data and regional criminal history data, are constructed in reverse order by a challenger to locate the authentic consumer against the location specified in the specific record tuple. In most circumstances, address information is regarded as one of the most sensitive aspects of the public record. Any association of such data with a publicly released identifier that has quasi-property has the potential to reveal a great regarding a user (that would normally remain concealed) or, in the worst-case scenario, ruin the user's social reputation. We determine the current situation in geospatial masking, undertake a thorough study of current masking approaches, as well as design three-layer iterative RDV masking, a viable technique that gives location secrecy without rendering publicly available data useless. The recommended approach is excellent for geo-referenced, consistent, granular point published data.

The current privacy protection methods adopted by social network providers rely on restricting users' access rights. They force a user to rigidly divide other users into two categories only: friends and strangers. Based on this classification, they prevent non-friends from accessing to the user's data while provide full access for friends regardless of how close they are to her. However, the level of privacy protection can be increased gradually and smoothly rather than firmly like a zero/one function. Moreover, the utility of social networks is reduced if we prevent data miners from computing global statistics by applying rigid privacy policies. In this paper, we present a distance-based location privacy protection system (DBLP2) to preserve location privacy of social network users based on their friendship distance. Whenever a user wants to see another user's location (in her profile or from spatiotemporal tags on her posts), the system returns a differentially private response based on their friendship distance. In our proposed system, a user's location information provided for other users is more generalized as the friendship distance increases. In other words, family members and close friends receive a more accurate response than casual friends and strangers. Through analysis, we show that our proposed system makes the process of location privacy protection more flexible in terms of friendship distances.

Edge computing, as a new computing paradigm, appears to solve the record generated by a great deal of edge devices that cannot be processed by traditional cloud computing. The real-time location big data of LBS (Location-Based Services) are transferred from the cloud server to the edge nodes, which further improves the service capacity and promotes the further development of LBS. However, based on the existing partition method, dynamic location data privacy cannot be well protected. In this paper, based on the differential privacy theory, we propose RPS (Random Partition and Selection) algorithm which can protect user's location information. We achieve this by designing a dynamic partition mechanism and noise adding algorithm based on the number of users. We repartition the region in each snapshot and give a new noise probability. Experiment results show that our proposed method has the significant progress in the loss of service quality than the existing partition release, such as UG (Uniform Grid) and AG (Adaptive Grid) mechanism.

With the pervasive use of smartphones that sense, collect, and process valuable information about the environment, ensuring location privacy has become one of the most important concerns in the modern age. A few recent research studies discuss the feasibility of processing data gathered by a smartphone to locate the phone's owner, even when the user does not intend to share his location information, e.g., when the Global Positioning System (GPS) is off. Previous research efforts rely on at least one of the two following fundamental requirements, which significantly limit the ability of the adversary: (i) the attacker must accurately know either the user's initial location or the set of routes through which the user travels and/or (ii) the attacker must measure a set of features, e.g., the device's acceleration, for potential routes in advance and construct a training dataset. In this paper, we demonstrate that neither of the above-mentioned requirements is essential for compromising the user's location privacy. We describe PinMe, a novel user-location mechanism that exploits non-sensory/sensory data stored on the smartphone, e.g., the environment's air pressure, along with publicly-available auxiliary information, e.g., elevation maps, to estimate the user's location when all location services, e.g., GPS, are turned off.

People nowadays rely heavily on technology and prefer being provided with personalized services. Location Based Services (LBS) are one of those highly popular services. Users provide their location information in some query to the LBS server, which in turn processes the query and returns personalized results. The major concern when dealing with these services is the privacy issue. Mainly, there are two privacy issues, the user's identity and the user's location. Not securing such information could result in some threats to the user. To preserve the user's privacy, researchers have proposed spatial cloaking to blur the user's location by a trusted location anonymizer server. Existing methods suffer from high communication cost due to the large number of communication rounds between the user's device and the cloud server to answer the query. In this paper, we propose an efficient k-anonymity algorithm, called Aman, to compute the cloaked area with minimal number of communication rounds between the user and the cloud server. Unlike existing methods in which the server starts the search from the root, or the leafs, of the indexing structure, Aman algorithm reduces the search time by starting the search at an intermediate estimated level of the indexing structure that is as close as possible to the queried location. To preserve the user's privacy, Aman uses k-anonymity cloaking to hide the user's location. The experimental results using synthetic and real datasets show that Aman outperforms other state-of-the-art approaches.

QoS (Quality of Service) (our approach can be applied to a wide variety of services; in this paper, we focus on Web services) performance is intensively relevant to locations due to the network distance and the Internet connection between users and services. Thus, considering the location information of services and users is necessary. However, the location information has been ignored by most previous work. In this paper, we take both services’ and users’ location information into account. Specifically, we propose a location‐aware QoS prediction approach, called LANFM, by exploiting neural network techniques and factorization machine to improve user‐perceived experience. First of all, the information (e.g., id and location) of services and users is expressed as embedding vectors by leveraging neural network techniques. Then, the inner product of various embedding vectors, along with the weighted sum of feature vectors, is used to predict the QoS values. It should be noted that the inner product operation could capture the interactions between services and users, which is helpful to predict QoS values of services that have not been invoked by users. A collection of extensive experiments have been carried out on a real‐world dataset to validate the effectiveness of the LANFM model.

Location-Based Services (LBSs) have been facilitating and enriching people’s daily lives. While users enjoy plenty of conveniences, privacy disclosure in terms of both location information and query contents is common. Most of the existing solutions mainly focus on location privacy and adopt K-anonymity principle to preserve user’s privacy. However, these methods are vulnerable to protect user’s query content. In this paper, we propose a Privacy Preserving and Content Protection (PPCP) scheme for LBSs users. Unlike most of researches requiring a trusted third party (TTP), our scheme is based on a semi-trusted middle entity, which is unaware of both the exact location information about issuer and query content in the user’s requirement. We utilize space filling curve to transform user location and protect user query content based on encryption technology, so that the proposed scheme can provide enhanced location privacy and query privacy protection in both snapshot and continuous LBSs.

As the access of web has been increased greatly for information retrieval and it is difficult to extract the relevant information in less time. Sometime Search engine does not able to understand user search intend behind query. Thus Query recommendation can be used to help user to state exactly their information need. Search engine can return appropriate result to meet users' information needs. There are various methods based on history of users and snippets to retrieve the information. But these methods fail to satisfy users need. Therefore in addition of history and snippets with users location information will produce better results. Snippet based method with incorporating users preferences and location results the URLs for given query, also it ranks the clicked URLs at the top of the result based on user profile. The performance of the system shows that the snippet based with incorporating users location give better and effective recommendation for all types queries as compared to previous methods including low frequency queries.

This article in view of the user's location information security sharing scheme analyzes the core technology and common sharing model, combined with the user's location information security core sharing scheme, including initialization processing, recording, location sharing, system security, etc., and its aim is to increase the use value of the sharing scheme, fully protect the user's information security.