ELRM: A Generic Framework for Location Privacy in LBS

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.

Nowadays, the location privacy problem has become an important problem for the users who enjoy the location-based services (LBSs). Researchers have focused on the problem of how to protect the location privacy of user efficiently for a long time. On one hand, many achievements adopt the centralized structure in which there is an additional center server. Additionally, some other researchers adopt the distributed structure to overcome the disadvantages brought by the center server in the centralized anonymous system structure. On the other hand, the existing methods of solving the problem are always to protect the individual user’s location privacy in LBSs, without considering the user group’s location privacy. This kind of methods is not very applicable to the status of a number of users who formed a group to complete a LBS task together by collaborative computing. In order to solve the problem of location privacy protection for a user group in the untrusted mobile social networks, a location privacy protection method based on the distributed structure is discussed in this paper. In the scheme, the special homomorphic features of BGN cryptosystem are cleverly used so that it can solve the group’s three classical location service applications simultaneously, namely, group nearest neighbor query, optimal group collection point determination, and group friend’s distance query, by only one security policy. If there are k users who formed the group, it could achieve k-anonymity without exposing the coordinate of each individual user or using any anonymous areas. Furthermore, theoretical and experimental analysis proves that the proposal can efficiently protect each user’s location privacy in the group through taking full advantage of the collaborative computing and communication capabilities of the mobile terminals. It can resist the existing distance interaction attack and collusion attack and can realize the secure and efficient fine-grained controllable location privacy protection for the user group.

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.

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.

Location Based Services (LBSs) introduce several privacy issues, the most relevant ones being: (i) how to anonymize a user; (ii) how to specify the level of anonymity; and, (iii) how to guarantee to a given user the same level of desired anonymity for all of his requests. Anonymizing the user within k potential users is a common solution to (i). A recent work [28] highlighted how specifying a practical value of k could be a difficult choice for the user, hence introducing a feeling based model: a user defines the desired level of anonymity specifying a given area (e.g. a shopping mall). The proposal sets the level of anonymity (ii) as the popularity of the area--popularity being measured via the entropy of the footprints of the visitors in that area. To keep the privacy level constant (iii), the proposal conceals the user requests always within an area of the same popularity--independently from the current user's position. The main contribution of this work is to highlight the importance of the time when providing privacy in LBSs. Further, we show how applying our considerations user privacy can be violated in the related model, but also in a relaxed one. We support our claim with both analysis and a practical counter-example.

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.

Location-based services (LBSs) are a crucial element of the Internet of Things (IoT) and have garnered significant attention from both researchers and users, driven by the rise of wireless devices and a growing user base. However, the use of LBS-enabled applications carries several risks, as users must provide their real locations with each query. This can expose them to potential attacks from the LBS server, leading to serious issues like the theft of personal information. Consequently, protecting location privacy is a vital concern. To address this, location dummy-based methods are employed to safeguard the location privacy of LBS users. However, location dummy-based approaches also suffer from problems such as low resistance against inference attacks and the generation of strong dummy locations, an issue that is considered an open problem. Moreover, generating many location dummies to achieve a high privacy protection level leads to high network overhead and requires high computational capabilities on the mobile devices of the LBS users, and such devices are limited. In this paper, we introduce the Caching-Aware Double-Dummy Selection (CaDDSL) algorithm to protect the location privacy of LBS users against homogeneity location and semantic location inference attacks, which may be applied by the LBS server as a malicious party. Then, we enhance the CaDDSL algorithm via encapsulation with agents to solve the tradeoff between generating many dummies and large network overhead by proposing the Cache-Aware Overhead-Aware Dummy Selection (CaOaDSL) algorithm. Compared to three well-known approaches, namely GridDummy, CirDummy, and Dest-Ex, our approach showed better performance in terms of communication cost, cache hit ratio, resistance against inference attacks, and network overhead.

Due to the explosive growth of location-detection devices, such as GPS (Global Positioning System), a user’ privacy threat is continuously increasing in location-based services (LBSs). However, the user must precisely disclose his/her exact location to the LBS while using such services. So, it is a key challenge to efficiently preserve a user’s privacy in LBSs. For this, the existing method employs a 2PASS cloaking framework that not only hides the actual user location but also reduces bandwidth consumption. However, it suffers from privacy attack. Therefore, we, in this paper, propose a density-based k-anonymization scheme using a weighted adjacency graph to preserve a user’s privacy. Our k-anonymization scheme can reduce bandwidth usages and efficiently support k-nearest neighbor queries without revealing the private information of the query initiator. We demonstrate from experimental results that our scheme yields much better performance than the existing one.KeywordscomponentPrivacy threatlocation-based services (LBS)location privacycloakingbandwidthk-anonymityweighted adjacency graph

The increasing availability of space-time trajectories left by location-aware devices is expected to enable novel classes of applications where the discovery of consumable, concise, and actionable knowledge is the key step. However, the analysis of mobility data is a critic task by the privacy point of view: in fact, the peculiar nature of location data might enable intrusive inferences in the life of the individuals whose data is analyzed. It is thus important to develop privacy-preserving techniques for the publication and the analysis of mobility data.This chapter provides a brief survey of the research on anonymity preserving data publishing of moving objects databases.While only few papers so far have tackled the problem of anonymity in the off-line case of publication of a moving objects database, rather large body of work has been developed for anonymity on relational data on one side, and for location privacy in the on-line, dynamic context of location based services (LBS), on the other side. In this chapter we first briefly review the basic concepts of k-anonymity on relational data. Then we focus on the body of research about privacy in LBS: we try to identify some useful concepts for our static context, while highlighting the differences, and discussing the inapplicability of some of the LBS solutions to the static case. Next we present in details some of the papers that recently have attacked the problem of moving objects anonymization in the static context. We discuss in details the problems addressed and the solutions proposed, highlighting merits and limits of each work, as well as the various problems still open.KeywordsLocation Base ServiceLocation PrivacyDistorted VersionAttack GraphPrivacy BreachThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Location Based Services (LBS) are becoming very popular with today’s users. Some of these LBS require users to continuously send requests for services. This lead to leakages of both location and query contents to malicious adversaries. Further, if users are constrained by the nature of the road networks, an adversary can follow their path trajectory with ease. Most of the current privacy preserving solutions focus on temporal and spatial cloaking based methods to protect users’ location privacy. However, these solutions are vulnerable when subjected to continuous query environments. In this paper, we propose an optimized solution that preserves privacy for users’ trajectory for continuous LBS queries in road networks. First, we deploy a trusted third party architecture to provide anonymity for users as they use LBS services. Second, we utilize mix zone techniques and design two algorithms. The first algorithm, Abstraction Graph (AG), selects a sample of mix zones that satisfy the user desired privacy level under the acceptable service availability condition. The second algorithm, Optimized Decision Graph (ODG), utilizes the generated graph to find an optimal solution for the placement of mix zones through decomposition, chunking and replacement strategies. Finally, we analyze the capability of our algorithms to withstand attacks prone to mix zones and carry out experiments to verify this. The experiments results show that our Algorithms preserve privacy for users based on their privacy and service availability conditions.

Location-based services are increasingly used in our daily activities. In current services, users however have to give up their location privacy in order to acquire the service.The literature features a large number of contributions which aim at enhancing user privacy in location-based services. Most of these contributions obfuscate the locations of users using spatial and/or temporal cloaking in order to provide k-anonymity. Although such schemes can indeed strengthen the location privacy of users, they often decrease the service quality and do not necessarily prevent the possible tracking of user movements (i.e., direction, trajectory, velocity). With the rise of Geofencing applications, tracking of movements becomes more evident since, in these settings, the service provider is not only requesting a single location of the user, but requires the movement vectors of users to determine whether the user has entered/exited a Geofence of interest.In this paper, we propose a novel solution, PrivLoc, which enables the privacy-preserving outsourcing of Geofencing and location-based services to the cloud without leaking any meaningful information about the location, trajectory, and velocity of the users. Notably, PrivLoc enables an efficient and privacy-preserving intersection of movement vectors with any polygon of interest, leveraging functionality from existing Geofencing services or spatial databases. We analyze the security and privacy provisions of PrivLoc and we evaluate the performance of our scheme by means of implementation. Our results show that the performance overhead introduced by PrivLoc can be largely tolerated in realistic deployment settings.KeywordsPrivacyGeofencinglocation trackinglocation-based services

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.

Location-based services have been increasingly integrated into people’s daily activities. However, some of these services may not be trustworthy and lead to serious privacy breaches. While spatial transformation techniques such as location perturbation or generalization have been studied extensively, many of them only consider the locationat single timestamps without considering temporal correlations among the locations of a moving user, leaving the user’s location with no guarantees of privacy protection against attacks that would exploit this vulnerability. This work proposes a new technique for preserving data privacy, named PrivLBS, which ensures that the individual’s location will not be easily re-identified by malicious services. Extensive simulation experiments have been carried out to evaluate the efficiency of PrivLBS. Experimental results show that PrivLBS reaches higher protection compared to other related approaches over different kinds of attacks.

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 (LBS) have the potential to make a profound impact on our lives. Many industry sources anticipate that the market for LBS will take off during 2002, with the roll out of GPRS being the driver, rather than that of 3G. The existing LBS infrastructure manufacturers include those who have developed the so-called 911 emergency location services, which have been implemented in the USA over the last year, as well as LBS services such as the marine weather forecasting service, Maritime Nowcasting Service (MNS). Weather data can be described as perishable, i.e., it is highly time-sensitive and its relevance and accuracy have rapid rates of decay. This is comparable with much of the anticipated content of future location based services (e.g., traffic congestion, availability of theatre seats or restaurant tables, the progress of a person, animal or consignment, etc.). The paper gives an insight into some of the infrastructure requirements for the processing and transport of perishable information within a LBS, based on four years experience of operating MNS.