Location-based Services Queries Research Articles

Geometric Searchable Encryption for Privacy-Preserving Location-Based Services

Location data play an important role in location-based services (LBS) since they can help a service provider analyze users' daily activities and further derive users' behavioral patterns. However, in the meanwhile the use of the location data in LBS can also enable the service provider to track users' journeys which will reveal their personal information, such as house address and places of interest they visited, all of which users are usually unwilling to disclose. Therefore, security and privacy incidents can and do occur often. In this paper, aiming at how to hide location privacy and meanwhile provide an accurate search for LBS, we design a new geometric searchable encryption scheme under a public-key system (a.k.a. asymmetric system) for different types of geometric range queries, which enables the service provider to respond to the range queries from users accurately without learning the information about users' location. Towards this goal, we first exploit a novel encoding, and then develop some sophisticated transformations in combination with a special pairing function, which eventually converts different types of geometric range queries in LBS into different inner product problems. Finally, we present our construction by virtue of the techniques above with the inner product encryption technique. In addition, a comprehensive experimental analysis running on a specific application of privacy-preserving LBS and comparison demonstrate, our proposal is able to guarantee a higher accuracy for users' range queries without any false positive, and meanwhile express richer queries. Furthermore, we show another application of our proposal to privacy-preserving remote medical diagnosis at the end of this paper.

A Novel Privacy-Preserving Location-Based Services Search Scheme in Outsourced Cloud

With the development of wireless communications and the pervasiveness of location-aware mobile electronic devices, location-based services (LBS) which can provide a convenient lifestyle for people have attracted considerable interest recently. However, there still exists the privacy disclosure problem of LBS today. To solve this problem, in this paper, we present a novel privacy-preserving LBS search scheme in outsourced cloud. In the proposed LBS search scheme, the LBS providers data are first outsourced to the cloud server in an encrypted method. Then, a registered user constructs a query model to obtain accurate LBS query results without divulging his/her location information and query attribute to the LBS provider and the cloud server. Specifically, based on the designed matrix encryption technology, the LBS search scheme can achieve privacy preservation of users query and confidentiality of LBS data in the outsourced cloud server. Through security analysis, we show that our scheme can resist various known security threats. The experimental results further show that our LBS search scheme greatly reduces the communication overhead and provides convenient search experience to the users.

Privacy-Preserving Spatio-Temporal Keyword Search for Outsourced Location-Based Services

With the popularization of location-based services (LBS), encryption techniques have been utilized to protect data security when outsourcing LBS to cloud. However, existing schemes only consider spatial range search or keyword search, while expressive and practical search over encrypted LBS data is still a challenging problem. In this article, we introduce PrivSTL, a privacy-preserving spatio-temporal keyword search framework over the encrypted LBS data based on attribute-based encryption, linear encryption and RSA encryption. It allows mobile users to submit LBS query with spatial range, time interval and Boolean keyword expression, and provides accurate and authorized search by matching these query conditions and also the access policy. Then we introduce an extended scheme PrivSTG, which utilizes Geohash to divide the locations into grids, and outsources an encrypted index tree to cloud servers. PrivSTG improves the service efficiency by searching only over the ciphertexts in the surrounding grids of mobile user. Finally, we analyze the security of PrivSTL against chosen-plaintext, chosen-keyword and outside keyword-guessing attacks in generic bilinear group model, and show that PrivSTL guarantees the spatio-temporal keyword profile privacy, and also protects the query privacy. The experimental results indicate that our scheme is practical and efficient for outsourced LBS.

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.

Preserving Location Privacy in the IoT against Advanced Attacks using Deep Learning

Location-based services (LBSs) have received a significant amount of recent attention from the research community due to their valuable benefits in various aspects of society. In addition, the dependency on LBS in the performance of daily tasks has increased dramatically, especially after the spread of the COVID-19 pandemic. LBS users use their real location to build LBS queries to take benefits. This makes location privacy vulnerable to attacks. The privacy issue is accentuated if the attacker is an LBS provider since all information about users is accessible. Moreover, the attacker can apply advanced attacks, such as map matching and semantic location attacks. In response to these issues, this work employs artificial intelligence to build a robust defense against advanced location privacy attacks. The key idea behind protecting the location privacy of LBS users is to generate smart dummy locations. Smart dummy locations are false locations with the same query probability as the real location, but they are far from both the real location and each other. Relying on the previous two conditions, the deep-learning-based intelligent finder ensures a high level of location privacy protection against advanced attacks. The attacker cannot recognize the dummies from the real location and cannot isolate the real location by a filtering process. In terms of entropy (the privacy protection metric), accuracy (the deep learning metric), and total execution time (the performance metric) and compared to the well-known DDA and BDA systems, the proposed system shows better results, where entropy = 15.9, accuracy = 9.9, and total execution time = 17 sec.

Toward Oblivious Location-Based k-Nearest Neighbor Query in Smart Cities

Enabled by the flourishing Internet-of-Things technology, smart cities can offer a variety of smart services to our daily lives and have received considerable attention in recent years. As a pivotal component of smart cities, location-based services (LBSs) have been deeply exploited by both academia and industry. Meanwhile, since cloud computing can provide reliable and flexible IT resources, many LBS services have been outsourced to the cloud for offering better services. Nevertheless, as the cloud is not fully trusted, privacy preservation becomes an essential requirement for these services. Over the past years, many privacy-preserving location-based k-nearest neighbor ( kNN) query schemes over the cloud have been proposed. However, most of them are subjected to an inevitable design defect, i.e., whenever a user queries twice at the same location, the cloud can identify and return the same query result to the query user, and such information together with third-party data breaches could be exploited by the cloud for some location disclosures. Although some existing schemes can cope with the issue, they are not quite practical, as they will bring heavy overheads on the query user side. In this article, aiming to address the above challenge, we propose a novel oblivious location-based kNN query scheme, in which the cloud cannot link two queries even if they are initiated by query users at the same location. Specifically, based on the modified Paillier cryptosystem, we first present three privacy-preserving protocols, namely, oblivious absolute value calculation, sorting, and top- k extraction. Then, by integrating these three protocols, we propose our novel oblivious location-based kNN query scheme. The detailed security analysis shows that our proposed scheme really enhances the privacy preservation in LBS queries. In addition, extensive performance analysis and experiments are conducted, and the results indicate that our proposed scheme is also efficient for the query user.

Privacy-Enhancing Preferential LBS Query for Mobile Social Network Users

While social networking sites gain massive popularity for their friendship networks, user privacy issues arise due to the incorporation of location-based services (LBS) into the system. Preferential LBS takes a user’s social profile along with their location to generate personalized recommender systems. With the availability of the user’s profile and location history, we often reveal sensitive information to unwanted parties. Hence, providing location privacy to such preferential LBS requests has become crucial. However, the current technologies focus on anonymizing the location through granularity generalization. Such systems, although provides the required privacy, come at the cost of losing accurate recommendations. Hence, in this paper, we propose a novel location privacy-preserving mechanism that provides location privacy through k-anonymity and provides the most accurate results. Experimental results that focus on mobile users and context-aware LBS requests prove that the proposed method performs superior to the existing methods.

Privacy-Preserving Location-Based Services Query Scheme Against Quantum Attacks

Location-based service (LBS) provides more and more conveniences to people. However, it also brings potential threats of offending users’ privacy. How to protect users’ privacy in LBS schemes has aroused increasing research interests in recent years. Most of the existing privacy-preserving LBS schemes are based on the hardness of traditional number-theoretic problems such as the integer factorization or the discrete logarithm problems. However, with the development of large scale quantum computers, these traditional problems can be easily solved by Shor's algorithms, hence the security of these LBS schemes is greatly threatened. In this paper, we solve this problem by constructing a privacy-preserving LBS scheme against quantum attacks from an LWE-based key-homomorphic pseudorandom functions (PRF). In our scheme, due to the key-homomorphic property of the PRF, an LBS user only has to compute one PRF value of the target location and the remaining computation is outsourced to a cloud server, which releases the user from heavy computation burden. In addition, by dividing the key encrypting LBS data into two parts and assigning the two parts to the cloud sever and each user respectively, our scheme avoids the threats of key abuse and information leaking of LBS data. Moreover, we use this PRF to realize an authenticated protocol, which protects the communications between the LBS users and the cloud server. We stress that the security of our scheme is based only on the security of the LWE-based key-homomorphic PRF, hence our scheme is the first LBS scheme secure against quantum attacks.

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.

Efficient Privacy-Preserving Scheme for Location Based Services in VANET System

A Vehicular Ad-hoc Network (VANET) is a type of Mobile Ad-hoc Network (MANET) that is used to provide communications between nearby vehicles, and between vehicles and fixed infrastructure on the roadside. VANET is not only used for road safety and driving comfort but also for infotainment. Communication messages in VANET can be used to locate and track vehicles. Tracking can be beneficial for vehicle navigation using Location Based Services (LBS). However, it can lead to threats on location privacy of vehicle users; since it can profile them and track their physical location. Therefore, to successfully deploy LBS, user's privacy is one of major challenges that must be addressed. In this paper, we propose Privacy-Preserving Fully Homomorphic Encryption over Advanced Encryption Standard (P 2 FHE-AES) scheme for LBS query. This scheme is required for location privacy protection to encourage drivers to use this service without any risk of being pursued. It is implemented using Network Simulator (NS-2), Simulation of Urban Mobility (SUMO), and Cloud simulation (CloudSim). Analysis and evaluation results demonstrate that P 2 FHE-AES scheme can preserve the privacy of the drivers' future routes in an efficient and secure way. The results prove the feasibility and efficiency of P 2 FHE-AES scheme in terms of query's response time, query accuracy, throughput and query overhead.

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.

Efficient LBS queries with mutual privacy preservation in IoV

Public awareness on privacy stimulates many researches about privacy-preserving location based services (LBS) in terms of providing mutual privacy to both LBS and its users. However, the high latency of privacy preservation in LBS becomes a main obstacle for applying LBS to Internet of Vehicles (IoV). To solve this problem, we propose two privacy-preserving LBS query schemes (kNN and T-kNN) by taking the advance of fog computing and by applying oblivious transfer (OT) and ciphertext-policy attribute based encryption (CP-ABE). Given a query from a vehicle, both schemes return k nearest POIs as response, with the difference that T-kNN supports fine-grained type based POI queries. Based on our proposed oblivious key transfer and privacy-preserving secret key generation, both schemes preserve mutual privacy of both LBS provider and vehicles. Complexity analysis and empirical study show that our approach outperforms the other two state-of-the-art works.

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.

P[formula omitted]GQ: A practical privacy-preserving generic location-based services query scheme

The Location-Based Services (LBS) enables people to find location places according to their interests. As the size of LBS dataset surprisingly increases, the LBS providers inevitably outsource their LBS dataset to a cloud server, but needs to encrypt it at first for preventing sensitive information leakage. However, designing a scheme that supports generic LBS query over encrypted data is still a challenging problem. Motivated by this, we present a practical privacy-preserving generic LBS query scheme (P3GQ), which achieves both data privacy and query privacy. In the scheme, a LBS provider outsources its dataset to a cloud server and relies on it to process LBS queries, but leaks no confidential information about both the dataset and queries to the cloud server. More specifically, we introduce a new tool called CircleTest: to test whether a point is inside of a given circular area or not, while keeps them secret. Hence, we obtain the P3GQ scheme to support for generic LBS query while achieving privacy-preserving based on the CircleTest building block and an encrypted inverted index technique. We analyze the privacy requirements of our scheme, and additionally conduct an experiment on both the building block CircleTest and the P3GQ scheme to show their practical performance.

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.

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.

Protecting query privacy with differentially private k-anonymity in location-based services

Nowadays, location-based services (LBS) are facilitating people in daily life through answering LBS queries. However, privacy issues including locationprivacy and queryprivacy arise at the same time. Existing works for protecting queryprivacy either work on trusted servers or fail to provide sufficient privacy guarantee. This paper combines the concepts of differential privacy and k-anonymity to propose the notion of differentially private k-anonymity (DPkA) for queryprivacy in LBS. We recognize the sufficient and necessary condition for the availability of 0-DPkA and present how to achieve it. For cases where 0-DPkA is not achievable, we propose an algorithm to achieve ??-DPkA with minimized ??. Extensive simulations are conducted to validate the proposed mechanisms based on real-life datasets and synthetic data distributions.

Cognitive Approach for Location Privacy Protection

While enjoying the convenience of location-based services (LBSs) in everyday life, wireless device users could also put their location privacy at risk. An untrusted LBS provider can store mobile users’ data on its server, track users in various ways or share users location data to the third parties. To protect LBS users’ privacy, many position confusion algorithms were proposed, but those algorithms often have difficulty balancing the utility-privacy tradeoffs. In this paper, we propose a new cognitive approach that enables near-complete privacy protection for LBS users by leveraging existing social network resources. We introduce a heterogeneous multi-server architecture that cuts off the direct connection between the LBS queries and the query issuers, and an auction-based incentive mechanism guaranteed user participation, which is critical for the success of the proposed architecture. A simulation system and a smartphone application were developed, and our evaluation results show that the proposed method can not only achieve the near-total privacy protection for LBS users, but also significantly improve the quality of the services.

An Efficient and Privacy-Preserving Multiuser Cloud-Based LBS Query Scheme

Location-based services (LBSs) are increasingly popular in today’s society. People reveal their location information to LBS providers to obtain personalized services such as map directions, restaurant recommendations, and taxi reservations. Usually, LBS providers offer user privacy protection statement to assure users that their private location information would not be given away. However, many LBSs run on third-party cloud infrastructures. It is challenging to guarantee user location privacy against curious cloud operators while still permitting users to query their own location information data. In this paper, we propose an efficient privacy-preserving cloud-based LBS query scheme for the multiuser setting. We encrypt LBS data and LBS queries with a hybrid encryption mechanism, which can efficiently implement privacy-preserving search over encrypted LBS data and is very suitable for the multiuser setting with secure and effective user enrollment and user revocation. This paper contains security analysis and performance experiments to demonstrate the privacy-preserving properties and efficiency of our proposed scheme.