Privacy In Location-based Services Research Articles

Progressive Collaborative Method for Protecting Users Privacy in Location-Based Services

The development of new mobile communication and information service technologies has opened up exciting possibilities for location-based services. Users of location-based services (LBS) can access vital data from their service providers by utilizing their location data. Maps and navigation, information services, tourist information services, social networking, and many more popular applications are available. A user's location and other personal details must be submitted to the providers of location-based services in order for them to work. For example, details about one's whereabouts and identity. By "location privacy," we mean the idea that third parties shouldn't be able to track a user's precise whereabouts. It is important that users' sensitive information be hidden from unauthorized individuals when communicating. Most difficult in LBS location-based are concerns about communications and data. Each peer does their duty reciprocally in a collaborative method, which is a completely distributed technique. For the most secure and private location-based services (LBS), it employs cryptographic methods. The number of people using LBS is growing at a rapid pace these days. At this time, there isn't a single method available that has scalability capabilities. Building a realistic and computationally efficient solution that offers high privacy while decreasing processing overhead and improving scalability is a challenging task. The suggested method is cost-effective, supports scaling, is highly resilient against security and privacy assaults, and ensures privacy.

A novel privacy protection scheme for location-based services using collaborative caching

Protecting privacy in Location-based services (LBSs) has been one of the major concerns over the last few years. Users’ queries can reveal sensitive information to LBSs, thus giving an opportunity for the adversaries to track the querying user. Existing location privacy protection mechanisms either use a trusted third party (TTP) or a location obfuscation-based scheme. TTP-based schemes require users to report their location-related information to the TTP before sending a query to the location-based service provider (LSP). Once the TTP is compromised, users’ privacy can be breached. On the other hand, location obfuscation schemes achieve privacy at the cost of accuracy. We address these problems by proposing a collaborative caching-based privacy-preserving scheme for LBSs. The scheme prevents the users’ location information leakage from untrusted communication channels as well as LSP while maintaining the quality of service. The scheme uses a novel dummy location generation technique and a virtual identity mechanism to improve users’ location and identity privacy. Furthermore, one-time spatial groups are created with the nearby neighbors to access the localized services, thus providing less interaction with the untrusted LSP. The proposed work has the potential to preserve users’ location privacy and reduce the computation and communication costs of the system. The aforementioned claims are verified by the experimental results.

Protecting user privacy in location-based services over road networks

ABSTRACT In location-based services (LBS), the users with mobile devices share information such as their locations and queries to obtain desired services. Additionally, LBS providers are often able to track or record the movements of their users with great spatial and temporal accuracy. However, user location information can be misused by LBS providers or attackers, which creates privacy problems for users. This raises concerns about privacy breaches; therefore, many approaches have been proposed to protect user privacy. Thus, in previous works, the authors have proposed approaches to maintain the privacy of mobile users in the Euclidean space. However, it is difficult to apply current methods of Euclidean space to the environment of the road networks due to the topological characteristics of the road networks. In this paper, we propose a new approach to preserve user privacy in LBS over road networks. Additionally, in this paper, we provide new methods for selecting segments and users on the road network to face the attacks. Moreover, to evaluate our work, we compared it with other works such as MCC, BATCH, V-DCA, D-TC, and GCA approaches. After comparing these works, our work offers a good balance between quality of service and privacy.

Constructing dummy query sequences to protect location privacy and query privacy in location-based services

Location-based services (LBS) have become an important part of people’s daily life. However, while providing great convenience for mobile users, LBS result in a serious problem on personal privacy, i.e., location privacy and query privacy. However, existing privacy methods for LBS generally take into consideration only location privacy or query privacy, without considering the problem of protecting both of them simultaneously. In this paper, we propose to construct a group of dummy query sequences, to cover up the query locations and query attributes of mobile users and thus protect users’ privacy in LBS. First, we present a client-based framework for user privacy protection in LBS, which requires not only no change to the existing LBS algorithm on the server-side, but also no compromise to the accuracy of a LBS query. Second, based on the framework, we introduce a privacy model to formulate the constraints that ideal dummy query sequences should satisfy: (1) the similarity of feature distribution, which measures the effectiveness of the dummy query sequences to hide a true user query sequence; and (2) the exposure degree of user privacy, which measures the effectiveness of the dummy query sequences to cover up the location privacy and query privacy of a mobile user. Finally, we present an implementation algorithm to well meet the privacy model. Besides, both theoretical analysis and experimental evaluation demonstrate the effectiveness of our proposed approach, which show that the location privacy and attribute privacy behind LBS queries can be effectively protected by the dummy queries generated by our approach.

PrivLBS: Preserving Privacy in Location-Based Services

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.

A Trajectory Substitution Privacy Protection Scheme in location-based services

Aimed at the disclosure risk of mobile terminal user’s location privacy in location-based services, a location-privacy protection scheme based on similar trajectory substitution is proposed. On the basis of the anonymized identities of users and candidates who request LBS, this scheme adopts trajectory similarity function to select the candidate whose trajectory is the most similar to user’s at certain time intervals, then the selected candidate substitutes user to send LBS request, so as to protect user’s privacy like identity, query and trajectory. Security analyses prove that this scheme is able to guarantee such security features as anonymity, non-forgeability, resistance to continuous query tracing attack and wiretapping attack. And the results of simulation experiment demonstrate that this scheme remarkably improve the optimal candidate’ trajectory similarity and selection efficiency.

Resilient Privacy Protection for Location-Based Services through Decentralization

Location-Based Services (LBSs) provide valuable services, with convenient features for mobile users. However, the location and other information disclosed through each query to the LBS erodes user privacy. This is a concern especially because LBS providers can be honest-but-curious , collecting queries and tracking users’ whereabouts and infer sensitive user data. This motivated both centralized and decentralized location privacy protection schemes for LBSs: anonymizing and obfuscating LBS queries to not disclose exact information, while still getting useful responses. Decentralized schemes overcome disadvantages of centralized schemes, eliminating anonymizers, and enhancing users’ control over sensitive information. However, an insecure decentralized system could create serious risks beyond private information leakage. More so, attacking an improperly designed decentralized LBS privacy protection scheme could be an effective and low-cost step to breach user privacy. We address exactly this problem, by proposing security enhancements for mobile data sharing systems. We protect user privacy while preserving accountability of user activities, leveraging pseudonymous authentication with mainstream cryptography. We show our scheme can be deployed with off-the-shelf devices based on an experimental evaluation of an implementation in a static automotive testbed.

A Survey of System Architectures, Privacy Preservation, and Main Research Challenges on Location-Based Services

Location-based services (LBSs) have become popular in recent years due to the ever-increasing usage of smart mobile devices and mobile applications through networks. Although LBS application provides great benefits to mobile users, it also raises a sever privacy concern of users due to the untrusted service providers. In the lack of privacy enhancing mechanisms, most applications of the LBS may discourage the user’s acceptance of location services in general, and endanger the user’s privacy in particular. Therefore, it is a great interest to discuss on the recent privacy-preserving mechanisms in LBSs. Many existing location-privacy protection-mechanisms (LPPMs) make great efforts to increase the attacker’s uncertainty on the user’s actual whereabouts by generating a multiple of fake-locations together with user’s actual positions. In this survey, we present a study and analysis of existing LPPMs and the state-of-art privacy measures in service quality aware LBS applications. We first study the general architecture of privacy qualification system for LBSs by surveying the existing framework and outlining its main feature components. We then give an overview of the basic privacy requirements to be considered in the design and evaluation of LPPMs. Furthermore, we discuss the classification and countermeasure solutions of existing LPPMs for mitigating the current LBS privacy protection challenges. These classifications include anonymization, obfuscation, and an encryption-based technique, as well as the combination of them is called a hybrid mechanism. Finally, we discuss several open issues and research challenges based on the latest progresses for on-going LBS and location privacy research.

A New Group Location Privacy-Preserving Method Based on Distributed Architecture in LBS

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.

H-LPS: a hybrid approach for user's location privacy in location-based services

Applications providing location-based services (LBS) have gained much attention and importance with the notion of the internet of things (IoT). Users are utilising LBS by providing their location information to third-party service providers. However, location data is very sensitive that can reveal user's private life to adversaries. The passive and pervasive data collection in IoT upsurges serious issues of location privacy. Privacy-preserving location-based services is a hot research topic. Many anonymisation and obfuscation techniques have proposed to overcome location privacy issue. In this paper, we have proposed a hybrid location privacy scheme (H-LPS), a hybrid scheme mainly based on obfuscation and collaboration for protecting user's location privacy while using location-based services. Obfuscation naturally degrades the quality of service, but provides more privacy as compared to anonymisation. Our proposed scheme, H-LPS, provides a very high level privacy yet providing a good accuracy for most of the users. Privacy level and service accuracy of H-LPS is compared with state of the art location privacy schemes and it is showed that H-LPS could be a candidate solution for preserving user's location privacy in location-based services.

H-LPS: a hybrid approach for user's location privacy in location-based services

H-LPS: a hybrid approach for user's location privacy in location-based services

Security and Privacy in Location-Based Services for Vehicular and Mobile Communications: An Overview, Challenges, and Countermeasures

Location-based services (LBSs) have gained popularity as a result of the advances in mobile and communication technologies. LBS provide users with relevant information based on their location. In spite of the desirable features provided by LBS, the geographic locations of users are not adequately protected. Location privacy is one of the major challenges in vehicular and mobile networks. In this paper, we analyze the security and privacy requirements for LBS in vehicular and mobile networks. Specifically, this paper covers privacy enhancing technologies and cryptographic approaches that provide location privacy in vehicular and mobile networks. The different approaches proposed in literature are compared and open research areas are identified.

Current Trends and Challenges in Location-Based Services

Location-based services (LBS) are a growing area of research. This editorial paper introduces the key research areas within the scientific field of LBS, which consist of positioning, modelling, communication, applications, evaluation, analysis of LBS data, and privacy and ethical issues. After that, 18 original papers are presented, which provide a general picture of recent research activities on LBS, especially related to the research areas of positioning, modelling, applications, and LBS data analysis. This Special Issue together with other recent events and publications concerning LBS show that the scientific field of LBS is rapidly evolving, and that LBS applications have become smarter and more ubiquitous in many aspects of our daily life.

Protecting Privacy in Location-Based Services Through Location Anonymization Using Cloaking Algorithms Based on Connected Components

With ever increasing advances in Information and Communication Technologies, location-based services (LBS) have obtained significant attention and popularity. However the services are provided at the cost of exposing the location-context of users, which exposes privacy and security threats to users. This paper presents an efficient location privacy-preserving solution based on location cloaking. Specifically to cloak location-contexts a new component based graph model called Cloaked Region Construction Algorithm (CRCA) is proposed, which can group users in the closer location proximity. To obtain an optimal cloaked region, heuristic search based on the center of spanning tree technique is used. Further, Dice’s coefficient is used to find LBS query similarities that help to avoid query similarity attacks. Through component based graph structure, optimal sub-group selection and query diversity, the proposed CRCA protects users’ location privacy to a greater extend while maintaining the quality of the query service. Experimental results show that CRCA performs well on providing high degree of privacy protection with a reasonably-sized cloaked region, and a high cloaking success rate.

Privacy Preservation in Location-Based Services

Based on spatial and temporal big data, location- based services (LBSs) obtain considerable attention due to their potential to make cities smarter. However, LBS provides the functionality by extracting personal data from users, which may incur privacy leakage risk, such as intrusion, theft, and unauthorized sale of sensitive information. Hence, the mass market share of LBS depends on how well the privacy of citizens can be protected. In this article, we specifically study the privacy issues in LBSs. In detail, we start with classifying different types of LBSs and analyzing the privacy leakage presented in each type. After summarizing the state-of-the-art work on location privacy and query privacy, we propose a query content preservation approach with the aim of providing accurate LBS answer with zero server knowledge on query content. Finally, we present some open problems that may foster future research on LBS privacy preservation.

From location to location pattern privacy in location-based services

Location privacy is extensively studied in the context of location-based services (LBSs). Typically, users are assigned a location privacy profile and the precise locations are cloaked so that the privacy profile is not compromised. Though being well-defined for snapshot location privacy, these solutions require additional precautions and patches in case of consecutive LBS requests on the user trajectory. The attacker can exploit some background knowledge like maximum velocity to compromise the privacy profile. To protect against this kind of location privacy attacks, PROBE (Damiani et al. in Trans Data Priv 3(2):123–148, 2010)-like systems constantly check location privacy violations and alter requests as necessary. Clearly, the location privacy is defined in terms of snapshot locations. Observing that there are usually user-specific movement patterns existing in the shared LBS requests, this work extends location privacy to location pattern privacy. We present a framework where user-specific sensitive movement patterns are defined and sanitized in offline and online fashions, respectively. Our solution uses an efficient dynamic programming approach to decide on and to prevent sensitive pattern disclosure. An extensive experimental evaluation has been carried out too.

$P^4QS$: A Peer-to-Peer Privacy Preserving Query Service for Location-Based Mobile Applications

The location-based services provide an interesting combination of cyber and physical worlds. However, they can also threaten the users' privacy. Existing privacy preserving protocols require trusted nodes, with serious security and computational bottlenecks. In this paper, we propose a novel distributed anonymizing protocol based on peer-to-peer architecture. Each mobile node is responsible for anonymizing a specific zone. The mobile nodes collaborate in anonymizing their queries, without the need of getting access to any information about each other. In the proposed protocol, each request will be sent with a randomly chosen ticket. The encrypted response produced by the server is sent to a particular mobile node (called broker node) over the network, based on the hash value of this ticket. The user will query the broker to get the response. All parts of the messages are encrypted except the fields required for the anonymizer and the broker. This will secure the packet exchange over the P2P network. The proposed protocol was implemented and tested successfully, and the experimental results showed that it could be deployed efficiently to achieve user privacy in location-based services.

Enhancing privacy through uniform grid and caching in location-based services

With the increasing popularity of location-based services (LBSs), there is a corresponding increase in the potential for location privacy leakage. Existing solutions generally introduce a fully-trusted third party between the users and the location service provider (LSP). However, such an approach offers limited privacy guarantees and incurs high communication overhead. Specifically, once a fully-trusted third party is compromised, user information would likely be exposed. In this paper, we propose a solution designed to enhance location privacy in LBSs. Our scheme is based on the uniform grid, and adopts both order-preserving symmetric encryption (OPSE) andk-anonymity technique. Thus, the anonymizer knows nothing about a user’s real location, and it can only implement simple matching and comparison operations. In our approach, we also employ an entity (hereafter referred to as the converter) to transform the user-defined grid structure into the uniform grid structure. This combined with the caching mechanism, allow us to avoid repeated queries from different users on the same query spatial region and consequently, reduce the overhead of the LBS server. The analysis and simulation results demonstrate that our proposal can effectively preserve a user’s location privacy, with reduced overheads at the anonymizer and the LBS server.

Robustness, Security and Privacy in Location-Based Services for Future IoT: A Survey

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.

Achieving Effective $k$ -Anonymity for Query Privacy in Location-Based Services

Location-based services (LBS) leveraged by ubiquitous mobile devices have brought great convenience to mobile users in various aspects, including communication, information exchange, social activities, and so on. However, privacy concerns arise at the same time, since the users need to submit their locations and query contents to the LBS servers. To this end, location privacy and query privacy have been recognized. In particular, this paper focuses on query privacy for preventing the leakage of users’ query contents. Cloaking region-based techniques using untrusted servers and client-based $k$ -anonymity approaches have been devised to preserve query privacy in LBS. However, these works suffer from single point of failure or insufficiency of query privacy. To address this issue, we investigate effective $k$ -anonymity-based solutions for query privacy in LBS. We formulate a probabilistic framework $\textit{P}k\textit{A}$ , under which $k$ -anonymity-based mechanisms can be initiated, and analyze a recent proposed algorithm $DLS$ as an instance of $\textit{P}k\textit{A}$ . An algorithm circle segment is presented to provide effective query privacy when query interests have similar prior probability. To obtain more effective query privacy in general cases, we propose two algorithms MEE and MER , which optimize two individual privacy metrics, denoted expected entropy and expected max-min ratio, adopted in this paper. We recognize two practical properties— $No~More~Leakage$ and $k$ - $Effectiveness$ for effective query privacy, and our proposed algorithms satisfy both of $No~More~Leakage$ and $k$ - $Effectiveness$ . We conduct evaluation based on real-life data sets and synthetic distributions of query interests, and the evaluation results demonstrate that our proposed algorithms produce significantly improved query privacy.