Privacy of Spatial Trajectories

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.

Recent advances in mobile communication and development of sophisticated equipments lead to the wide spread use of Location Based Services (LBS). A major concern for large-scale deployment of LBSs is the potential abuse of their client location data, which may imply sensitive personal information. Protecting location information of the mobile user is challenging because a location itself may reveal user identity. Several schemes have been proposed for location cloaking. In our paper, we propose a generic Enhanced LBS Reference Model (ELRM), which describes the concept, the architecture and the functionalities for location privacy in LBS. As per the architecture, the system ensures location privacy, without trusting anybody including the peers or LBS servers. The system is fully distributed and evaluation shows its efficiency and high level of privacy with QoS.

Location-based services are becoming popular for mobile users. The mobile users' location plays a key role to provide the service from one side, but it can be considered as a dimension of their privacy and so necessary to keep it anonymous to the other parties. Since one important issue is to achieve an accurate service, it is important to use the mobile's accurate location. Using the location accurately raises some concerns on behalf of the user's privacy. One solution for meeting this requirement is using tickets by the means of a third party. The tickets should have some properties for not letting a mobile user to cheat and use the ticket more than one time. This paper proposes a protocol to preserve Preserve Privacy in Location based services, in short 2PLoc, aimed to provide anonymity of location, for location based services, based on onetime tickets regardless of the existence of any trusted third party. The protocol satisfies the requirement of accurate location use, as well as the ability of revoking anonymity on the ticket double spending. The user, location-based service provider, and the ticket issuer are the three untrusted parties in 2PLoc. 2PLoc is based on a special designed ticket that disconnects the relation between the location of the mobile user and its identity. The ticket is designed based on the blind signature and the concept of elliptic curves discrete logarithm.

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.

Location based services (LBS) are distributed multi-party infrastructures with the opportunity to generate profitable m-business applications. Also, LBS pose a great risk for their users' privacy, as they have access to private information about a person's whereabouts in a particular context. As location data is the essential input for LBS, protection of privacy in LBS is not a problem of providing anonymity by supressing data release, but a problem of identity management and control over information handling. In this paper we show a solution that ensures a LBS user's control over location information with managed identities. At the same time, our solution enables profitable business models, e.g. for mobile telephony operators. Additionally, we provide fine-grained consent management for the LBS user.

Recent development in wireless communication technology and mobile equipment is making location-based services (LBSs) more popular day by day. However, because users continuously send queries to a server by using their exact locations in the LBSs, private information can be in danger. Therefore, a mechanism for users’ privacy protection is required for the safe and comfortable use of LBSs. For this, we, in this paper, propose a grid-based cloaking area creation scheme in order to support continuous queries in LBSs. Our scheme creates a cloaking area rapidly by using grid-based cell expansion to efficiently support the continuous LBSs. In addition, to generate a cloaking area which lowers the exposure probability of a mobile user to a minimum level, our scheme computes a privacy protection degree by granting weights to the mobile users. Finally, we show from our performance analysis that our cloaking scheme shows better performance than the existing cloaking scheme.KeywordsPrivacy protectionContinuous QueriesCloaking scheme

Location positioning by GPS has become a standard function in modern handheld device specifications. Even in indoor environments, positioning by utilizing signals from the mobile cellular network and the wireless LAN has been intensively studied. This chapter starts with some review of the state-of-the-art technologies. Positioning technologies propel the market of location-based services (LBS). They are mobile content services that provide location-related information to users. However, to enjoy these LBS services, the mobile user must explicitly expose his/her accurate location to the service provider, who might abuse such location information or even trade it to unauthorized parties. To protect privacy, traditional approaches require a trusted middleware on which user locations are anonanonymous ymized. This chapter presents two new privacy-preserving approaches without such a middleware. The first is a non-exposure location cloaking protocol where only relative distances are exchanged. The second is a protocol for nearest neighbor search with controlled location exposure.

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.

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.

The usage of Location-Based Services (LBS) holds a potential privacy issue when people exchange their locations for information relative to these locations. While most people perceive these information exchange services as useful, others do not, because an adversary might take advantage of the users' sensitive data. In this paper, we propose k-path, an algorithm for privacy protection for continuous location tracking-typed LBS. We take inspiration in k-anonymity to hide the user location or trajectory among k locations or trajectories. We introduce our simulator as a tool to test several strategies to hide users' locations. Afterwards, this paper will give an evaluation about the effectiveness of several approaches by using the simulator and data provided by the GeoLife data set.

To address the risk of location privacy disclosure in location-based services (LBSs), existing solutions generally introduce a single trusted anonymizer between the users and the location service provider (LSP). However, a single anonymizer offers limited privacy guarantees and incurs high communication overhead in continuous LBSs. Once the anonymizer is compromised, it may put the user information in jeopardy. In this paper, we propose a dual privacy preserving (DPP) scheme in continuous LBSs to protect the users' trajectory and query privacy. Our scheme introduces multiple anonymizers between the users and LSP, and combines with Shamir threshold mechanism and K-anonymity technology to improve the users' trajectory and content privacy in continuous LBSs. An anonymizer alone cannot get the users' trajectory and query contents, and it thus can be semitrusted. Our scheme can enhance the users' privacy and solve the single point failure in single anonymizer structure. The security analysis demonstrate that the proposed scheme has the ability to protect users' trajectory and content privacy effectively.

With the widespread use of mobile devices, location-based services (LBSs), which provide useful services adjusted to users’ locations, have become indispensable to our daily lives. However, along with several benefits, LBSs also create problems for users because to use LBSs, users are required to disclose their sensitive location information to the service providers. Hence, several studies have focused on protecting the location privacy of individual users when using LBSs. Geo-indistinguishability (Geo-I), which is based on the well-known differential privacy, has recently emerged as a de-facto privacy definition for the protection of location data in LBSs. However, LBS providers require aggregate statistics, such as user density distribution, for the purpose of improving their service quality, and deriving them accurately from the location dataset received from users is difficult owing to the data perturbation of Geo-I. Thus, in this study, we investigated two different approaches, the expectation-maximization (EM) algorithm and the deep learning based approaches, with the aim of precisely computing the density distribution of LBS users while preserving the privacy of location datasets. The evaluation results show that the deep learning approach significantly outperforms other alternatives at all privacy protection levels. Furthermore, when a low level of privacy protection is sufficient, the approach based on the EM algorithm shows performance results similar to those of the deep learning solution. Thus, it can be used instead of a deep learning approach, particularly when training datasets are not available.

Internet of Things (IoT) connects sensing devices to the Internet for the purpose of exchanging information. Location information is one of the most crucial pieces of information required to achieve intelligent and context-aware IoT systems. Recently, positioning and localization functions have been realized in a large amount of IoT systems. However, security and privacy threats related to positioning in IoT have not been sufficiently addressed so far. In this paper, we survey solutions for improving the robustness, security, and privacy of location-based services in IoT systems. First, we provide an in-depth evaluation of the threats and solutions related to both global navigation satellite system (GNSS) and non-GNSS-based solutions. Second, we describe certain cryptographic solutions for security and privacy of positioning and location-based services in IoT. Finally, we discuss the state-of-the-art of policy regulations regarding security of positioning solutions and legal instruments to location data privacy in detail. This survey paper addresses a broad range of security and privacy aspects in IoT-based positioning and localization from both technical and legal points of view and aims to give insight and recommendations for future IoT systems providing more robust, secure, and privacy-preserving location-based services.

Privacy preservation in location based services (LBS) has received extensive attention in recent years. One of the less explored problems in this domain is associated with services that rely on continuous updates from the mobile object. Cloaking algorithms designed to hide user locations in single requests perform poorly in this scenario. The historical k-anonymity property is therefore enforced to ensure that all cloaking regions include at least k objects in common. However, the mobility of the objects can easily render increasingly bigger cloaking regions and degrade the quality of service. To this effect, this paper presents an algorithm to efficiently enforce historical k-anonymity by partitioning of an object’s cloaking region. We further enforce some degree of directional similarity in the k common peers in order to prevent an excessive expansion of the cloaking region.

With the advancement of intelligent transportation systems, location-based services (LBS) have been widely applied in vehicular ad hoc networks (VANETs). LBS utilizes mobile devices to gather vehicle location data, which is then processed using relevant technologies. By combining this data with additional information, LBS offers users personalized and intelligent services. However, providing LBS brings critical security issues related to the exposure of vehicle positions, as well as privacy-preserving problems during the process of collecting location information in VANETs. We propose a distributed trust-based k anonymity scheme to address the aforementioned issues. Our proposed scheme adopts a trust framework among vehicles for various types of LBS. This framework involves a multiparty evaluation and consideration of trust value fluctuations to enhance the efficiency of establishing a reliable k anonymous cloaking region. Furthermore, by leveraging the tamper-proof and decentralized nature of blockchain, we employ a lightweight consortium blockchain to maintain the security of the trustworthiness data throughout the entire model. Extensive security analysis and rigorous experiments have been conducted to demonstrate that the scheme exhibits a certain degree of resilience against attacks on various trust models. Additionally, it has the ability to construct anonymous regions with limited time delay, thereby preserving the privacy of vehicle locations. In comparison to other schemes, it exhibits lower computational complexity and enhanced security.