Location Privacy Preserving Scheme Research Articles

LPP-BPSI: A location privacy-preserving scheme using blockchain and Private Set Intersection in spatial crowdsourcing

Leakage of location information can lead to malicious attacks on workers in spatial crowdsourcing (SC) and loss of security. In response, a location privacy preserving scheme based on blockchain and Private Set Intersection were proposed, which reduced the computational overhead of workers by using a cloud service provider for outsourcing secure computation while ensuring their location privacy. First, three smart contracts based on blockchain were designed that could eliminate the control of traditional spatial crowdsourcing servers on the task allocation process. Second, differential privacy of the location method was proposed to construct the privacy location, and the cloud service provider used the privacy location to perform the location matching calculation. Third, an improved variant of the Private Set Intersection algorithm was constructed for the case of matching multiple workers for a single task in spatial crowdsourcing, which could efficiently perform location matching while protecting location privacy. To further reduce the computational overhead, Bloom filters were utilized to filter out a large number of free workers with zero location relevance before location matching. The experimental results showed that the scheme was robust and practical in the case of a large number of workers and concurrent task requests.

A K-Anonymous Location Privacy-Preserving Scheme for Mobile Terminals

Mobile terminals boost the prosperity of location-based service (LBS) which have already involved in every aspect of People's daily life and are increasingly used in various industries. Aimed at solving the security and efficiency problem in the existing location privacy protection schemes, a K-anonymity location privacy preservation scheme based on mobile terminal is proposed. Firstly, number of rational dummy locations is selected from the cloaking region, from which more favorable locations are further filtered according to location entropy, so a better anonymity effect can be achieved. Secondly, the secure and efficient m-out-of-n oblivious transfer protocol is adopted, which not only avoids the dependency on the trusted anonymity center in existing schemes to improve the efficiency, but also meets the requirements for querying multiple interest points at one time. Security analyses demonstrate that this scheme satisfies such security properties as anonymity, non-forgeability and resistance to replay attack, and simulation results show that this scheme has higher execution efficiency and privacy level, while is low in communications costs.

A Caching-Based Dual K-Anonymous Location Privacy-Preserving Scheme for Edge Computing

Location-based services have become prevalent and the risk of location privacy leakage increases. Most existing schemes use third-party-based or third-party-free system architectures; the former suffers from a single point of failure (SPOF) and the latter experiences a heavy load on user terminal equipment and higher communication costs. Ensuring location privacy while lowering system overhead becomes a challenge. Many existing schemes fail to leverage the responses from a LBS server; such responses can be cached to answer subsequent queries. As a result, providing users with a relatively comprehensive level of location privacy protection is troublesome. In this article, we propose a caching-based dual K-anonymous (CDKA) location privacy preserving scheme in edge computing environments. Our scheme employs an edge server to intercedes between a user and LBS the server. We reduce the load on the user device by applying multi-level caching and to protect location privacy through dual anonymity. To ensure the location privacy in our construction, we set mobile clients and edge servers as anonymous. We use caching to lower the communication overhead and enforce the location privacy. The security analysis of our scheme supports its robustness against the edge server and the LBS server privacy offenses. We rely on the computation time, the communication cost and the cache hit ratio to evaluate our work against existing constructions. The results are two-fold: our work possesses a better response rate down to 15 ms to 32.6 ms and exhibits lower communication cost requirements down to 6.2 and 38.9 KB compared to existing works. Our scheme witnesses a higher cache hit ratio up to 13.6% and 39.1% compared to the literature.

Location Privacy-Preserving Scheme in IoBT Networks Using Deception-Based Techniques

The Internet of Battlefield Things (IoBT) refers to interconnected battlefield equipment/sources for synchronized automated decision making. Due to difficulties unique to the battlefield, such as a lack of infrastructure, the heterogeneity of equipment, and attacks, IoBT networks differ significantly from regular IoT networks. In war scenarios, real-time location information gathering is critical for combat effectiveness and is dependent on network connectivity and information sharing in the presence of an enemy. To maintain connectivity and guarantee the safety of soldiers/equipment, location information must be exchanged. The location, identification, and trajectory of soldiers/devices are all contained in these messages. A malicious attacker may utilize this information to build a complete trajectory of a target node and track it. This paper proposes a location privacy-preserving scheme in IoBT networks using deception-based techniques. Dummy identifier (DID), sensitive areas location privacy enhancement, and silence period concepts are used to minimize the attacker's ability to track a target node. In addition, to consider the security of the location information, another security layer is proposed, which generates a pseudonym location for the source node to use instead of its real location when sending messages in the network. We develop a Matlab simulation to evaluate our scheme in terms of average anonymity and probability of linkability of the source node. The results show that the proposed method improves the anonymity of the source node. It reduces the attacker's ability to link the old DID of the source node with its new DID. Finally, the results show further privacy enhancement by applying the sensitive area concept, which is important for IoBT networks.

Knowledge-Driven Location Privacy Preserving Scheme for Location-Based Social Networks

Location privacy-preserving methods for location-based services in mobile communication networks have received great attention. Traditional location privacy-preserving methods mostly focus on the researches of location data analysis in geographical space. However, there is a lack of studies on location privacy preservation by considering the personalized features of users. In this paper, we present a Knowledge-Driven Location Privacy Preserving (KD-LPP) scheme, in order to mine user preferences and provide customized location privacy protection for users. Firstly, the UBPG algorithm is proposed to mine the basic portrait. User familiarity and user curiosity are modelled to generate psychological portrait. Then, the location transfer matrix based on the user portrait is built to transfer the real location to an anonymous location. In order to achieve customized privacy protection, the amount of privacy is modelled to quantize the demand of privacy protection of target user. Finally, experimental evaluation on two real datasets illustrates that our KD-LPP scheme can not only protect user privacy, but also achieve better accuracy of privacy protection.

Blockchain-enabled anonymous mutual authentication and location privacy-preserving scheme for [formula omitted] networks

Fifth-generation (5G) networks are about to become the backbone of communications through their integration with many services such as software-defined network (SDN), network function virtualization (NFV), cloud computing, and multiple input multiple outputs (MIMO). However, this integration brings major security and privacy challenges to 5G networks. This paper proposes an authentication protocol based on pseudonyms and blockchain to achieve security and privacy for 5G networks. Security analysis shows that the proposed scheme is secure and preserves privacy against different types of attacks. Furthermore, performance evaluation demonstrates the efficiency of the proposed scheme compared to existing schemes.

Personalized Location Privacy With Road Network-Indistinguishability

The proliferation of location-based services (LBS) leads to increasing concern about location privacy. Location obfuscation is a promising privacy-preserving technique but yet to be adequately tailored for vehicles in road networks. Existing obfuscation schemes are based primarily on the Euclidean distances and can lead to infeasible results, e.g., off-road locations. In this paper, we define Road Network-Indistinguishability (RN-I) to evaluate obfuscation-based location privacy-preserving schemes in road networks. To protect drivers’ location privacy in road networks, we propose a Personalized Location Privacy-Preserving (PLPP) scheme and prove it achieves RN-I. The PLPP scheme employs a dual-obfuscation algorithm, consisting of a connection perturbation and an interval perturbation, to obfuscate on-road locations. An efficient personalization algorithm is designed for the PLPP scheme to fine-tune location privacy budgets for capturing drivers’ sensitive locations and privacy requirements. Experiments upon two real-world datasets confirm the location privacy-preserving capability, data utility, and efficiency of the proposed PLPP scheme.

A Blockchain-Based Location Privacy-Preserving Scheme in Location-Based Service

With the rapid popularization and development of location-based services (LBS), location privacy preserving has become a mainstream concern. Existing anonymity-based methods rely mainly on anonymous location selection based on geographic information, which neglects the semantic information on the location, and behavior of self-interested users may raise a privacy vulnerability. A blockchain-based approach for location privacy protection schemes has been proposed to address these issues. In this scheme, the privacy level of the location is improved using private chain-based collaborative anonymous communication with anonymous set construction methods that consider the semantic diversity associated with the related user. Compared with other existing methods, evaluation based on real-world datasets shows that the proposed method prevents location privacy leakage from collaborative users and constructs a semantical anonymity set, thus effectively protecting the user’s location privacy.

Obfuscation-based location privacy-preserving scheme in cloud-enabled internet of vehicles

Obfuscation-based location privacy-preserving scheme in cloud-enabled internet of vehicles

CSLPPS: Concerted Silence-Based Location Privacy Preserving Scheme for Internet of Vehicles

The Internet of Vehicles (IoV) connects vehicles together and also to other internet of things components such as sensors and smart devices. The huge amount of data generated by these components is stored in the cloud, so does the services offered which are not limited to safety applications but also to infotainment where most of these services use the position and identity of the vehicle. Due to these requirements, the privacy of road users is at threat. Thus, preserving it is highly important as it is directly related to their safety. If a specific user is targeted on the road by an attacker who knows his/her identity and real-time location, the consequences can vary from simple stalking to assassination. This paper proposes a new solution called Concerted Silence-based Location Privacy Preserving Scheme for Internet of Vehicles (CSLPPS) that ensures the anonymous and unlinkable participation in the IoV location-based services and vehicular networks safety applications. This scheme relies on the synchronization of the identifier change between the cooperative vehicles to enter a silent period simultaneously before continuing their cyber-activity with new identifiers. The simulation and study of performance proved efficiency of CSLPPS against a modeled global passive attacker and when compared to state-of-art solution.

WHISPER: A Location Privacy-Preserving Scheme Using Transmission Range Changing for Internet of Vehicles.

Internet of Vehicles (IoV) has the potential to enhance road-safety with environment sensing features provided by embedded devices and sensors. This benignant feature also raises privacy issues as vehicles announce their fine-grained whereabouts mainly for safety requirements, adversaries can leverage this to track and identify users. Various privacy-preserving schemes have been designed and evaluated, for example, mix-zone, encryption, group forming, and silent-period-based techniques. However, they all suffer inherent limitations. In this paper, we review these limitations and propose WHISPER, a safety-aware location privacy-preserving scheme that adjusts the transmission range of vehicles in order to prevent continuous location monitoring. We detail the set of protocols used by WHISPER, then we compare it against other privacy-preserving schemes. The results show that WHISPER outperformed the other schemes by providing better location privacy levels while still fulfilling road-safety requirements.

Preserving Location Privacy for Outsourced Most-Frequent Item Query in Mobile Crowdsensing

The emergence of mobile crowdsensing (MCS) has provided us with unprecedented opportunities for both sensing coverage and data transmission. However, in many MCS applications, the MCS workers are usually required to report the location information of the assigned tasks, which inevitably reveals the workers' location information, even trajectories, and severely impedes the popularization of the MCS system. It is believed that the query on the most-frequent location, e.g., querying the most congested location over a period in a city, is one of the most popular statistics queries in the MCS system, but it may disclose workers' location information. To address the issue, in this article, we propose a location privacy-preserving scheme for outsourced most-frequent item query in the MCS system, where two noncollusive semi-trusted cloud servers cooperatively handle the most-frequent item query. Specifically, by employing our pseudonymization mechanism, transposition cipher, ciphertext packing technique, and order-preserving merge function, our proposed scheme can efficiently answer the most-frequent item query while ensuring the privacy of both workers' personal information and query results. Detailed security analysis shows that our proposed scheme is privacy-preserving. In addition, extensive experiments are conducted, and the results show that our proposed scheme outperforms alternative schemes in terms of computational costs and communication overhead.

LocJury: An IBN-Based Location Privacy Preserving Scheme for IoCV

Stemming from the recent evolutionary progress of the 5G wireless communication and Internet of Things (IoT) relevant technologies, the vision of the Internet of Connected Vehicles (IoCV) has become more apparent. On the basis of the state-of-the-art IoCV conceptual implementations, the location of the vehicles is one of the essential driven data of IoCV, and the location privacy issue needs to be taken into account. However, when scrutinizing into IoCV, noticeable challenges of location-aware scenario has raised. For IoCV, location is more than just query criteria like in Location-based Services (LBSs) of mobile Internet. It is also the underpinning data of various types of IoCV underlying mechanisms and functions. This difference makes preserving location privacy in IoCV quite different from the traditional privacy scenarios. In this paper, an overall analysis of end-user location privacy in IoCV was performed. To solve the location privacy dilemma, we proposed an intent prediction-based approach named LocJury, which benefits from the emerging concept of Intent-based Networking (IBN). LocJury provides location privacy by learning and estimate the intent of location access and will penalize those malicious location accesses. By simulating the conceptual IBN-based IoCV application scenario, which relies on the location accesses, the performance of LocJury is evaluated under various circumstances. The simulation result verified the effectiveness of our proposed method.

An Efficient Dummy-Based Location Privacy-Preserving Scheme for Internet of Things Services

With the rapid development of GPS-equipped smart mobile devices and mobile computing, location-based services (LBS) are increasing in popularity in the Internet of Things (IoT). Although LBS provide enormous benefits to users, they inevitably introduce some significant privacy concerns. To protect user privacy, a variety of location privacy-preserving schemes have been recently proposed. Among these schemes, the dummy-based location privacy-preserving (DLP) scheme is a widely used approach to achieve location privacy for mobile users. However, the computation cost of the existing dummy-based location privacy-preserving schemes is too high to meet the practical requirements of resource-constrained IoT devices. Moreover, the DLP scheme is inadequate to resist against an adversary with side information. Thus, how to effectively select a dummy location is still a challenge. In this paper, we propose a novel lightweight dummy-based location privacy-preserving scheme, named the enhanced dummy-based location privacy-preserving(Enhanced-DLP) to address this challenge by considering both computational costs and side information. Specifically, the Enhanced-DLP adopts an improved greedy scheme to efficiently select dummy locations to form a k-anonymous set. A thorough security analysis demonstrated that our proposed Enhanced-DLP can protect user privacy against attacks. We performed a series of experiments to verify the effectiveness of our Enhanced-DLP. Compared with the existing scheme, the Enhanced-DLP can obtain lower computational costs for the selection of a dummy location and it can resist side information attacks. The experimental results illustrate that the Enhanced-DLP scheme can effectively be applied to protect the user’s location privacy in IoT applications and services.

RPAR: Location Privacy Preserving via Repartitioning Anonymous Region in Mobile Social Network

Applying the proliferated location-based services (LBSs) to social networks has spawned mobile social network (MSN) services that allow users to discover potential friends around them. While enjoying the convenience of MSN services, the mobile users also are confronted with the risk of location disclosure, which is a severe privacy preserving concern. In this paper, we focus on the problem of location privacy preserving in MSN. Particularly, we propose a repartitioning anonymous region for location privacy preserving (RPAR) scheme based on the central anonymous location which minimizes the traffic between the anonymous server and the LBS server while protecting the privacy of the user location. Furthermore, our scheme enables the users to get more accurate query results, thus improving the quality of the location service. Simulation results show that our proposed scheme can effectively reduce the area of anonymous regions and minimize the traffic.

A Location Privacy Preserving Scheme Based on Repartitioning Anonymous Region in Mobile Social Network

Applying the proliferated location-based services (LBS) to social networks has spawned mobile social network (MSN) services that it allows users to discover potential friends around them. In this paper, we focus on the problem of location privacy preserving in MSN. Particularly, we propose a location privacy preserving (RPAR) scheme via to repartition anonymous region where the central anonymous location minimizes the traffic between the anonymous server and the LBS server while protecting the privacy of the user location.

Energy-Efficient Location Privacy Preserving in Vehicular Networks Using Social Intimate Fogs

Although the ways to protect vehicular location privacy have been actively studied in recent years, the locations of vehicles are frequently submitted for authentication during accessing location-based services (LBS), which makes it easier for attackers to launch attacks by threaten the location privacy of vehicles. Moreover, the rapid deployment of electric vehicles requires location privacy-preserving to be more energy-efficient. In this paper, we proposed a novel energy-efficient location privacy preserving (ELPP) scheme in vehicular networks using social intimate fogs (SIFs). ELPP is based on three novel techniques. First, ELPP deal with large bursts of LBS requests by transferring them to their SIF. The key insight is that computing efficiency for location privacy preserving can be gained at the price of some communication delay. Second, ELPP introduces a travel plan-based LBS content pre-caching mechanism, which enables quick and exible retrieval and distribution of the LBS contents. Third, to guarantee confidentiality, ELPP randomly encrypted the data by access control encryption. ELPP is deployable on existing devices, without modifying protocols in vehicular networks. We present an implementation case of ELPP and validate that it is energy-efficient, fast, and secure.

A Location Privacy-Preserving Scheme for VANETs Based on Virtual Mix Zone

The location of vehicles, which may contain some sensitive information, is critical for VANETs security.The literature location privacy-preserving scheme suffers from either selfish vehicle node orvehicle density issues. In this paper, a novel location privacy-preserving scheme for VANETs isproposed based on virtual mix zone which is created dynamically when the pseudonyms of vehiclenodes are going to be expired. Moreover, a reputation model and mechanism is introduced to encouragethe selfish node to join the mix zone. The evaluation and analysis show that the proposedscheme performs better than the typical ones in terms of privacy strength and energy cost.

Enhanced Location Privacy Preserving Scheme in Location-Based Services

With the increasing popularity of mobile communication devices loaded with positioning capabilities (e.g., GPS), there is growing demand for enjoying location-based services (LBSs). An important problem in LBSs is the disclosure of a user's real location while interacting with the location service provider (LSP). To address this issue, existing solutions generally introduce a trusted Anonymizer between the users and the LSP. However, the introduction of an Anonymizer actually transfers the security risks from the LSP to the Anonymizer. Once the Anonymizer is compromised, it may put the user information in jeopardy. In this paper, we propose an enhanced-location-privacy-preserving scheme for the LBS environment. Our scheme employs an entity, termed Function Generator, to distribute the spatial transformation parameters periodically, with which the users and the LSP can perform the mutual transformation between a real location and a pseudolocation. Without the transforming parameters, the Anonymizer cannot have any knowledge about a user's real location. The main merits of our scheme include the following: 1) no fully trusted entities are required, and 2) each user can obtain accurate points of interest while preserving location privacy. The efficiency and effectiveness of the proposed scheme are validated by extensive experiments. The experimental results show that the proposed scheme preserves location privacy at low computational and communication cost.

Performance Analysis of Location Privacy Preserving Scheme for MANETs

We consider the problem of preserving node's location privacy which is essential for minimizing the attacks during multi-hop routing in MANETs. As by intercepting and analyzing the transmitted packets, intermediate nodes (conventionally, assumed to be trustworthy) can track sender and receiver nodes, and can also trace the route between them. Earlier approaches attempts to preserve the location privacy by changing node-IDs or masking the location information, but results in high cost and degradation of service quality. To overcome these drawbacks, this paper presents: 1) a novel rough set based Location Privacy Preserving (LPP) scheme during route establishment; and 2) an efficient Data Transfer scheme for Location Privacy Preserving based Routes (DTLPPR) during data transfer. Analysis of trustworthiness of neighbor nodes using Discrete Time Markov Chain, and proposed scheme in terms of location privacy and route untraced ability is presented. Analytical work is validated using simulations.