2Ploc: Preserving Privacy in Location-based Services

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 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.

Location-based services (LBSs) typically crowdsource geo-tagged data from mobile users. Collecting more data will generally improve the utility for LBS providers; however, it also leads to more privacy exposure of users’ mobility patterns. Although the tension between data utility and user privacy has been recognized, there lacks a solution that determines how much data to collect—in both spatial and temporal domains—is the “best” for both mobile users and the service provider. This article proposes a strategy toward making an optimal tradeoff such that a user submits data only if her mobility privacy will not be compromised and the data utility of the LBS provider will be sufficiently improved. To this end, we first define and formulate a concept called privacy exposure , which incorporates both the spatial distribution and the temporal transition of a user’s activity points . Second, we define and quantify data utility in terms of spatial repetitions and temporal closeness among data based on an economic principle. Then, we propose a PRivacy-preserving and UTility-Enhancing Crowdsourcing (PRUTEC) algorithm to determine, on behalf of each mobile user, whether a newly sensed piece of data should be submitted to the LBS provider. Our simulation demonstrates that PRUTEC improves the data utility of the service provider with a much less amount of data to collect and reduces privacy exposure for mobile users while collecting useful data continuously.

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 (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.

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.

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.

Preserving user's privacy has recently drawn special attention in the field of location-based services and many techniques such as k-anonymity or obfuscation have been suggested to protect user's privacy. All of these traditional techniques are, however, geometry-based and separated from the database level. This separation causes the query processing to involve in two phases, querying the database to retrieve the exact locations of users and then modifying them to decrease the quality of this information. This two-phase process is time-consuming due to the number of disk accesses required to retrieve the user's exact location. Also, these geometry-based techniques cannot guarantee location privacy when the adversary gains knowledge about the geography of the obfuscated region. We address these problems by proposing Hilbert-based framework for preserving user's privacy and B

In Location Based Services (LBSs), service providers can obtain mobile users' locations or traces while receiving their service requests. K-anonymity, which is the most commonly used location privacy protection method, needs the cooperation among mobile users to form a k-anonymous group. Though several incentive mechanisms have been proposed to motivate mobile users to participate in the k-anonymous group, most of them rely on a `trustful' center. In this paper, we propose a distributed secure incentive mechanism that applies blockchain smart contracts to motivate users for k-anonymity cooperation. To protect the reward transaction information, group encryption and blind collective signature are adopted. The simulations demonstrate the effectiveness of our proposed incentive mechanism.

Most of the mobile phones have GPS sensors which make location based service (LBS) applicable. LBS brings not only convenience but also location privacy leak to us. Achieving anonymity and sending private queries are two main privacy-preserving courses in LBS. A novel location privacy-preserving method is proposed based on Voronoi graph partition on road networks. Firstly, based on the prediction of a user's moving direction, a cooperative k-anonymity method is proposed without constructing cloaking regions which may lead to efficiency decline in continuous query. And then, a query algorithm is proposed without providing any user's actual location, replaced by continuous anchor sequence, to LBS provider. This algorithm can work out precise results according to candidate sets returned by LBS provider and it also solves uneven distribution problem in SpaceTwist. Performance analysis and experiments show that our method achieves a preferable tradeoff between QoS and location privacy preserving; it has obvious advantages compared with other methods.

Mobile communication networks are evolving towards smaller cells, higher throughput, better security and provision of better services. Wireless short-range technologies, such as the WLAN 802.11 standards family and Bluetooth, are expected to play a major role in future networks. The mobile core network is changing from circuit-switched to packet-switched technology and evolving to an IP core network based on IPv6. The IP core network will allow all IP devices to be connected seamlessly. Due to the movement detection mechanism of Mobile IPv6, mobile terminals will periodically update their current point of attachment to the network and hence provide the current location of the mobile user automatically. The convergence of wireless short-range networks, mobile networks and Internet technology will provide the mobile user's location without any add-in equipment for location measurement. The concept of mobile location services over the next generation IP networks is described. We also discuss the effectiveness of the short-range wireless network regarding a mobile user's position inside buildings and hotspot areas.

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.

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

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.

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