Continuous Location-based Services Research Articles

DPPS: A novel dual privacy-preserving scheme for enhancing query privacy in continuous location-based services

DPPS: A novel dual privacy-preserving scheme for enhancing query privacy in continuous location-based services

A real‐time privacy‐preserving scheme based on grouping queries for continuous location‐based services

SummaryWith the advancement of global positioning systems and communication technologies, location‐based services (LBS) have become widely used. However, user location privacy is vulnerable during service exchange, and location‐related queries may result in serious privacy disclosure issues. In this article, considering that attackers may make use of auxiliary information to track a mobile user, we propose a real‐time privacy‐preserving scheme based on the grouping of queries in continuous LBS. In this scheme, we first design the probability density functions to select virtual locations. Then, we group these virtual locations and the query location of the user into several queries. Simultaneously, we send these grouping queries to the LBS server. Following the steps outlined above, grouping queries can be used to conceal the true query. Moreover, the results of security analyses and performance evaluations show that the proposed scheme is effective and secure.

A Vehicle Trajectory Privacy Preservation Method Based on Caching and Dummy Locations in the Internet of Vehicles.

In the internet of vehicles (IoVs), vehicle users should provide location information continuously when they want to acquire continuous location-based services (LBS), which may disclose the vehicle trajectory privacy. To solve the vehicle trajectory privacy leakage problem in the continuous LBS, we propose a vehicle trajectory privacy preservation method based on caching and dummy locations, abbreviated as TPPCD, in IoVs. In the proposed method, when a vehicle user wants to acquire a continuous LBS, the dummy locations-based location privacy preservation method under road constraint is used. Moreover, the cache is deployed at the roadside unit (RSU) to reduce the information interaction between vehicle users covered by the RSU and the LBS server. Two cache update mechanisms, the active cache update mechanism based on data popularity and the passive cache update mechanism based on dummy locations, are designed to protect location privacy and improve the cache hit rate. The performance analysis and simulation results show that the proposed vehicle trajectory privacy preservation method can resist the long-term statistical attack (LSA) and location correlation attack (LCA) from inferring the vehicle trajectory at the LBS server and protect vehicle trajectory privacy effectively. In addition, the proposed cache update mechanisms achieve a high cache hit rate.

Location-Query-Privacy and Safety Cloaking Schemes for Continuous Location-Based Services

Nowadays, people can access a wide range of applications and services for mobile device users. Among them, location-based services (LBS), where the application needs the user’s position to provide the service. Some examples of these applications are Uber and Waze. Nevertheless, the repetitive use of an LBS can reveal confidential user information; thus, behavior patterns—such as daily routes—could be deduced by some dishonest LBS. Furthermore, a query’s keywords could provide information about a user’s health status or future position when it inquires about hospitals or hotels. Therefore, an adversary can use this information for unethical purposes, and users need mechanisms that protect their privacy. At present, several approaches separately tackle location privacy, location security, and query privacy. To the best of our knowledge, no previous work deals with all these mentioned aspects simultaneously especially when users demand continuous protection when moving and accessing an LBS. This paper proposes two batch techniques to provide location privacy, location safety, and query privacy in an environment that considers a continuous LBS. These techniques apply l -diversity (query privacy) in a context that contemplates query semantics, as well as a diverse set of users’ paths. Extensive experimentation shows that both techniques are cost-effective and scalable solutions that offer unified location privacy, query privacy, and location safety protection for many mobile users.

Trajectory Privacy Preserving for Continuous LBSs in VANET

Location-based services (LBSs) support various applications in vehicular ad hoc network (VANET). However, location/trajectory privacy becomes a serious concern for LBSs. Existing location/trajectory privacy-preserving schemes rarely take the attack model of adversaries into consideration, and the cost for achieving privacy has not been carefully studied. To deal with these problems, this study proposes a collaborative trajectory obfuscation scheme based on analyzing the attack model and designs a privacy-preserving efficiency metric that balances the achieved privacy and the cost. Through simulation, the effects of the density of vehicles using the same LBSs on the performance of our design and an existing scheme are investigated. The performance comparison results validate the effectiveness and efficiency of our scheme.

Protecting Spatiotemporal Event Privacy in Continuous Location-Based Services

Location privacy-preserving mechanisms (LPPMs) have been extensively studied for protecting users' location privacy by releasing a perturbed location to third parties such as location-based service providers. However, when a user's perturbed locations are released continuously, existing LPPMs may not protect the sensitive information about the user's real-world activities, such as “visited hospital in the last week” or “regularly commuting between location A and location B every weekday” (it is easy to infer that location A and location B may be home and office), which we call it spatiotemporal event. In this paper, we first formally define spatiotemporal event as Boolean expressions between location and time predicates, and then we define ε-spatiotemporal event privacy by extending the notion of differential privacy. Second, to understand how much spatiotemporal event privacy that existing LPPMs can provide, we design computationally efficient algorithms to quantify the spatiotemporal event privacy leakage of state-of-the-art LPPMs. It turns out that the existing LPPMs may not adequately protect spatiotemporal event privacy. Third, we propose a framework, PriSTE, to transform an existing LPPM into one protecting spatiotemporal event privacy by calibrating the LPPM's privacy budgets. Our experiments on real-life and synthetic data verified that the proposed method is effective and efficient.

Delay‐aware privacy‐preserving location‐based services under spatiotemporal constraints

SummaryThe ubiquitous use of location‐based services (LBS) through smart devices produces massive amounts of location data. An attacker, with an access to such data, can reveal sensitive information about users. In this paper, we study location inference attacks based on the probability distribution of historical location data, travel time information between locations using knowledge of a map, and short and long‐term observation of privacy‐preserving queries. We show that existing privacy‐preserving approaches are vulnerable to such attacks. In this context, we propose a novel location privacy‐preserving approach, called KLAP, based on the three fundamental obfuscation requirements: minimum k‐locations, l‐diversity, and privacy area preservation. KLAP adopts a personalized privacy preference for sporadic, frequent, and continuous LBS use cases. Specifically, it generates a secure concealing region (CR) to obfuscate the user's location and directs that CR to the service provider. The main contribution of this work is twofold. First, a CR pruning technique is devised to establish a balance between privacy and delay in LBS usage. Second, a new attack model called a long‐term obfuscated location tracking attack, and its countermeasure is proposed and evaluated both theoretically and empirically. We assess KLAP with two real‐world datasets. Experimental results show that it can achieve better privacy, reduced delay, and lower communication costs than existing state‐of‐the‐art methods.

Location Privacy-Preserving Method Based on Historical Proximity Location

With the rapid development of Internet services, mobile communications, and IoT applications, Location-Based Service (LBS) has become an indispensable part in our daily life in recent years. However, when users benefit from LBSs, the collection and analysis of users’ location data and trajectory information may jeopardize their privacy. To address this problem, a new privacy-preserving method based on historical proximity locations is proposed. The main idea of this approach is to substitute one existing historical adjacent location around the user for his/her current location and then submit the selected location to the LBS server. This method ensures that the user can obtain location-based services without submitting the real location information to the untrusted LBS server, which can improve the privacy-preserving level while reducing the calculation and communication overhead on the server side. Furthermore, our scheme can not only provide privacy preservation in snapshot queries but also protect trajectory privacy in continuous LBSs. Compared with other location privacy-preserving methods such as k-anonymity and dummy location, our scheme improves the quality of LBS and query efficiency while keeping a satisfactory privacy level.

A Survey on User’s Location Detail Privacy-Preserving Models

Protecting personal information of users is a challenging task in the area of computer network security in the recent days. Personal information of users is highly essential to provide location-based services (LBS), and at the same, it is important to protect it from unauthorized users. Users cannot avoid the sharing of location information in the current era of social networks. In order to provide continuous LBS, personal data of the users are required. When users want to use social networks, they worry about preserving their privacy. Users normally prefer to maintain their location secure and safe in social networks for many privacy reasons. Security cracks in social network lead to tracking of user location, and they may misuse user’s data. In this paper, we present different techniques that are used to protect location privacy of the users. Also it helps us to select an appropriate technique to protect location information of the user.

OMCPR: Optimal Mobility Aware Cache Data Pre-fetching and Replacement Policy Using Spatial K-Anonymity for LBS

Location-based services are an important category of context-aware computing, which play an important role in providing the continuous, local and spatially confined information systems very efficiently and accurately to their clients. The key component in location-based services is the current location of a mobile user. Thus, to protect mobile users’ location and their other information to an untrusted party with consideration of minimal waiting time, Optimal Mobility Aware Cache data Pre-fetching and Replacement policy (OMCPR) is being proposed here. In this continuous location-based services model, the system introduces a mediator namely Anonymizer by employing the prefetching facility for spatial K-anonymity that resides in between the user and Query Analyser to form a cloaking region using mobile user inputs (data freshness, the contribution rate of cell’s cache and location). It provides high-quality lossless location-based services by the utilization of the frequent pattern mining of mobile users’ trajectories to forecast their next position as per mobility and multiple constraints. A client–server based queueing model is used to simulate the proposed OMCPR platform. It provides higher privacy protection than the current state of the art strategies available, also minimizes the overhead of the LBS server and waiting time of mobile users by the addition of the prefetching facilities to Anonymizer.

PriSTE

Location privacy-preserving mechanisms (LPPMs) have been extensively studied for protecting a user's location in location-based services. However, when user's perturbed locations are released continuously, existing LPPMs may not protect users' sensitive spatiotemporal event , such as "visited hospital in the last week" or "regularly commuting between location 1 and location 2 every morning and afternoon" (it is easy to infer that locations 1 and 2 may be home and office). In this demonstration, we demonstrate PriSTE for protecting spatiotemporal event privacy in continuous location release. First, to raise users' awareness of such a new privacy goal, we design an interactive tool to demonstrate how accurate an adversary could infer a secret spatiotemporal event from a sequence of locations or even LPPM-protected locations. The attendees can find that some spatiotemporal events are quite risky and even these state-of-the-art LPPMs do not always protect spatiotemporal event privacy. Second, we demonstrate how a user can use PriSTE to automatically or manually convert an LPPM for location privacy into one protecting spatiotemporal event privacy in continuous location-based services. Finally, we visualize the trade-off between privacy and utility so that users can choose appropriate privacy parameters in different application scenarios.

A trajectory privacy-preserving scheme based on a dual-K mechanism for continuous location-based services

Location-based services (LBSs) have increasingly provided by a broad range of devices and applications, but one associated risk is location disclosure. To solve this problem, a commonly method is to adopt K-anonymity in the centralized architecture based on a single trusted anonymizer. However, this strategy may compromise user privacy involving continuous LBSs. In this study, we propose a dual-K mechanism (DKM) to protect the users’ trajectory privacy for continuous LBSs. The proposed DKM method firstly inserted multiple anonymizers between the user and the location service provider (LSP), and K query locations are sent to different anonymizers to achieve K-anonymity. Simultaneously, we combined the dynamic pseudonym and the location selection mechanisms to improve user trajectory privacy. Hence, neither the LSP nor the anonymizer can obtain the user trajectory. Security analyses demonstrates that our proposed scheme can effectively enhance user trajectory privacy protection, and the simulation results prove that the DKM scheme can preserve user trajectory privacy with low overhead on a single anonymizer.

Trajectory Protection Schemes Based on a Gravity Mobility Model in IoT

With the proliferation of the Internet-of-Things (IoT), the users’ trajectory data containing privacy information in the IoT systems are easily exposed to the adversaries in continuous location-based services (LBSs) and trajectory publication. Existing trajectory protection schemes generate dummy trajectories without considering the user mobility pattern accurately. This would cause that the adversaries can easily exclude the dummy trajectories according to the obtained geographic feature information. In this paper, the continuous location entropy and the trajectory entropy are defined based on the gravity mobility model to measure the level of trajectory protection. Then, two trajectory protection schemes are proposed based on the defined entropy metrics to protect the trajectory data in continuous LBSs and trajectory publication, respectively. Experimental results demonstrate that the proposed schemes have a higher level than the enhanced dummy-location selection (enhance-DLS) scheme and the random scheme.

A caching and spatial [formula omitted]-anonymity driven privacy enhancement scheme in continuous location-based services

With the rapid pervasion of location-based services (LBSs), protection of location privacy has become a significant concern. In most continuous LBSs’ privacy-preserving solutions, users need to transmit the location query data to an untrusted location service provider (LSP) to obtain query results, and the users discard these results immediately after using them. This results in an ineffective use of these results by future queries and in turn leads to a higher risk to user privacy from the LSP. To address these issues, we generally use caching to cache the query results for users’ future queries. However, the minimization of the interaction between users and LSPs is a challenge. In this paper, we propose an enhanced user privacy scheme through caching and spatial K-anonymity (CSKA) in continuous LBSs; it adopts multi-level caching to reduce the risk of exposure of users’ information to untrusted LSPs. In continuous LBS queries, our scheme first utilizes the Markov model to predict the next query location according to the user mobility. Then, according to the predicted location, cell’s cache contribution rate, and data freshness, an algorithm for forming spatial K-anonymity is designed to improve the user’s cache hit rate and enhance the user location privacy. The security analysis and simulation results demonstrate that our proposed CSKA scheme can provide higher privacy protection than a few previous methods, and it can minimize the overhead of the LBS server.

Multidimensional privacy preservation in location-based services

Users of mobile communication services can reap great benefits and take advantage of the convenience offered by various location-based services (LBSs). Privacy disclosure in LBSs poses a serious threat to the user’s security, and thus, existing solutions adopt K-anonymity principles to protect the location privacy of users. However, existing solutions have rarely considered the comprehensive protection of user privacy offered by LBSs such as location privacy, trajectory privacy, and query privacy. In this paper, we propose a multidimensional privacy preservation (MPP) scheme that provides full protection for user privacy without any need for a trusted third party (TTP). The MPP scheme employs a semi-trusted middle entity to perform user anonymization and result-blind filtering while unaware of any sensitive information regarding the mobile users. We utilize the Hilbert curve to transform user locations, and preserve users’ query contents using encryption technology. The proposed scheme provides enhanced security protection in both snapshot and continuous LBSs. Extensive experiments were conducted to verify the effectiveness and efficiency of the proposed scheme.

A Dual Privacy Preserving Scheme in Continuous Location-Based Services

With the development of wireless communication and positioning technology, location-based services (LBSs) have been gaining tremendous popularity, due to its ability to greatly facilitate the people’s daily lives. Meanwhile, it also entails the risk of location privacy disclosure. To address this issue, general solutions 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, dynamic pseudonym 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 semi-trusted. Our scheme can enhance the users’ privacy and effectively solve the single point of failure in single anonymizer structure. At the same time, the query authentication can guarantee the correctness of the query results. The analysis and simulation results demonstrate that the proposed scheme has the ability to protect users’ trajectory and content privacy effectively, and to reduce the computation and communication overhead of the single anonymizer.

ILLIA: Enabling <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math> </inline-formula>-Anonymity-Based Privacy Preserving Against Location Injection Attacks in Continuous LBS Queries

With the increasing popularity of location-based services (LBSs), it is of paramount importance to preserve one’s location privacy. The commonly used location privacy preserving approach, location ${k}$ -anonymity, strives to aggregate the queries of ${k}$ nearby users within a so-called cloaked region via a trusted third-party anonymizer. As such, the probability to identify the location of every user involved is no more than ${1/k}$ , thus offering privacy preservation for users. One inherent limitation of ${k}$ -anonymity, however, is that all users involved are assumed to be trusted and report their real locations. When location injection attacks (LIAs) are conducted, where the untrusted users inject fake locations (along with fake queries) to the anonymizer, the probability of disclosing one’s location privacy could be greatly more than ${1/k}$ , yielding a much higher risk of privacy leakage. To tackle this problem, in this paper we present ILLIA, the first work that enables ${k}$ -anonymity-based privacy preservation against LIA in continuous LBS queries. Central to the ILLIA idea is to explore the pattern of the users’ mobility in continuous LBS queries. With a thorough understanding of the users’ mobility similarity, a credibility-based ${k}$ -anonymity scheme is developed, such that ILLIA is able to defense against LIA without requiring in advance knowledge of how fake locations are manipulated while still maintaining high quality of services. Both the effectiveness and the efficiency of ILLIA are validated by extensive simulations on real world dataset loc-Gowalla.

RobLoP: Towards Robust Privacy Preserving Against Location Dependent Attacks in Continuous LBS Queries

With the increasing popularity of location-based services (LBS), how to preserve one’s location privacy has become a key issue to be concerned. The commonly used approach $k$ -anonymity, originally designed for protecting a user’s snapshot location privacy, inherently fails to preserve the user from location-dependent attacks (LDA) that include the maximum movement boundary (MMB) attacks and maximum arrival boundary (MAB) attacks, when the user continuously requests LBS. This paper presents RobLoP, a robust location privacy preserving algorithm against LDA in continuous LBS queries. The key insight of RobLoP is to theoretically derive the constraints of both MMB and MAB in a uniform way. It provides a necessary condition of the pairwise user to be safely cloaked against LDA. On top of that, RobLoP first identifies those candidate users who can be cloaked with the requesting user. RobLoP then searches for a so-called strict point set including the candidate set and other auxiliary points, as a sufficient condition under which RobLoP can finally generate the cloaked region successfully. To the best of our knowledge, RobLoP is the first work that can preserve location privacy against LDA thoroughly and closely with a theoretical guarantee. The effectiveness and superiority of RobLoP to state-of-the-art studies are validated via extensive simulations on the real trucks data, the synthetic data, as well as the measured data collected by ourselves.

An adaptive geo-indistinguishability mechanism for continuous LBS queries

The popularity of mobile devices with positioning capability and Internet accessibility in recent years has led to a revolution in the Location-based services (LBSs) market. Unfortunately, without preserving the user’s location privacy, LBS providers can collect and log the accurate location data of the service users and provide them to third parties. Many mechanisms have been proposed to preserve the LBS user’s location privacy. These mechanisms provide a partial disclosure of the user’s location. While said mechanisms have had demonstrable effectiveness with snapshot queries, the shortcoming of supporting continuous queries is their main drawback. Geo-indistinguishability represents a formal notion of obfuscation-based location privacy which protects the user’s accurate location while allowing an adequate amount of information to be released to get the service with an accepted utility level. Despite its effectiveness and simplicity, geo-indistinguishability does not address the potential correlation of the subsequent locations reported within the continuous queries. In this paper, we investigate the effect of exploiting the correlation of the user’s obfuscated locations on the location privacy level. We propose an adaptive location preserving privacy mechanism that adjusts the amount of noise required to obfuscate the user’s location based on the correlation level with its previous obfuscated locations. The experiments show that adapting the noise based on the correlation level leads to a better performance by applying more noise to increase the location privacy level when required or by reducing the noise to improve the utility level.

Hiding Yourself Behind Collaborative Users When Using Continuous Location-Based Services

The prosperity of location-based services (LBSs) makes more and more people pay close attention to personal privacy. In order to preserve users privacy, several schemes utilized a trusted third party (TTP) to obfuscate users, but these schemes were suspected as the TTP may become the single point of failure or service performance bottleneck. To alleviate the suspicion, schemes with collaborative users to achieve [Formula: see text]-anonymity were proposed. In these schemes, users equipped with short-range communication devices could communicate with adjacent users to establish an anonymous group. With this group, the user can obfuscate and hide herself behind at least [Formula: see text] other users. However, these schemes are usually more efficient in snapshot services than continuous ones. To cope with the inadequacy, with the help of caching in mobile devices, we propose a query information blocks random exchange and results caching scheme (short for CaQBE). In this scheme, a particular user is hidden behind collaborative users in snapshot service, and then the caches further preserve the privacy in continuous service. In case of the active adversary launching the query correlation attack and the passive adversary launching the impersonation attack, a random collaborative user selection and a random block exchange algorithm are also utilized. Then based on the feature of entropy, a metric to measure the privacy of the user against attacks from the active and passive adversaries is proposed. Finally, security analysis and experimental comparison with other similar schemes further verify the optimal of our scheme in effectiveness of preservation and efficiency of performance.