Location Verification Research Articles

Towards Building a Faster and Incentive Enabled Privacy-Preserving Proof of Location Scheme from GTOTP

In recent years, there has been significant growth in location-based services (LBSs) and applications. These services empower users to transmit their location data to location service providers, thereby facilitating the provisioning of pertinent resources and services. However, in order to prevent malicious users from sending fake location data, users must attest to their location for service providers, namely, through a proof of location (PoL). Such a proof should additionally prevent attackers from being able to obtain users’ identity and location information through it. In this paper, we propose an efficient privacy-preserving proof of location (PPPoL) scheme. The scheme is based on the standard cryptographic primitives, including Group Time-based One-Time Password (GTOTP) and public key encryption, which achieves entity privacy, location privacy, and traceability. Unlike the previous GTOTP-based PPPoL scheme, our scheme enables instant location verification with additional hash operations. To encourage the active participation of witnesses in location proofs, we propose an incentive mechanism based on smart contracts. Additionally, we implement a proof of concept of our PPPoL scheme on an Android device. Our experimental results show that proof generation and verification time are on the order of milliseconds. Meanwhile, the total overhead for the incentive mechanism amounts to 0.0011 ETH. This result is practical for mobile device-based LBSs.

Scaling the Timing-Based Detection of Anomalies in Real-World Aircraft Trajectories

The insecurity of ADS-B and GPS is a well-known problem in air traffic control. In this work, we introduce a statistical location verification method for crowdsourced aircraft data. We implement this method on the backend of the OpenSky Network and evaluate it on 8 months of data collected from Radarcape sensors. We thus demonstrate the effectiveness of our method in finding anomalous flight data in faster than real time. While we do not find clear evidence of widespread malicious spoofing attacks captured by OpenSky sensors, many sensor and transponder issues are unearthed. Finally, we simulate ADS-B and GNSS attacks, which were detected successfully with few false positives.

A new location verification protocol and blockchain-based drone rental mechanism in smart farming

In smart farming, information and communication technologies such as IoT are driving the agricultural industry towards improved efficiency and productivity while reducing human intervention. As agricultural systems become increasingly connected to the Internet, they provide many new opportunities but also introduce new challenges. Connecting such systems to a network exposes them to cyberattacks and vulnerabilities; therefore, security and privacy are important challenges that should be addressed. Specifically, because of the distinct characteristics of smart farming such as environmental and farm conditions, security solutions suitable for smart farming are indispensable. In this paper, we propose a location verification (LV) protocol as a security solution for autonomous and intelligent moving devices such as drones and robots deployed in smart farming. Moreover, as an application of the proposed LV protocol, we propose a blockchain-based secure drone rental mechanism for smart farming. We present a security analysis in which the proposed LV protocol and rental mechanism are secure against man-in-the-middle, impersonation, modification, replay, and relay attacks, with the rental mechanism providing mutual authentication. Through a performance evaluation of the LV protocol in terms of the communication overhead, computation cost, and storage cost and a performance evaluation of the blockchain for drone rental with regard to throughput and latency, we confirm the feasibility and efficiency of the LV protocol and drone rental mechanism in the smart farming.

A Location-Aware Verifiable Outsourcing Data Aggregation in Multiblockchains

With the rapid development of the Internet of Vehicles (IoV), location-aware outsourcing data aggregation is evolving into a fundamentally key role for analyzing a significant amount of data among smart devices. Due to the spatial property of the data in IoV, location privacy and data security in outsourcing data aggregation face critical challenges. Meanwhile, because of the diversity of entities in IoV, how aggregating data from multiple domains is also a serious issue. In this article, we propose a location-aware verifiable outsourcing data aggregation (LAVODA) for IoV where aggregators are hierarchical for the cross-chain mechanism in multiblockchains. With homomorphic encryption and homomorphic commitment, we achieve the verifiability of the aggregation while ensuring data confidentiality. Specifically, we combine twin-DH with circle-based location verification to ensure the privacy of the requester’s location strategy and data providers’ locations. The security analysis shows that our scheme can achieve the above secure properties. In addition, the experimental results demonstrate that our scheme is efficient and feasible in practice.

Received signal strength-based location verification technique in Wireless Sensor Network using Spline curve

Received signal strength-based location verification technique in Wireless Sensor Network using Spline curve

Utilizing Multi-Dimensional MmWave MIMO Channel Features for Location Verification

In this paper, we address the problem of authenticating transmitters in millimeter-wave (mmWave) multiple-input multiple-output (MIMO) communication systems, and propose a location verification scheme based on multi-dimensional mmWave MIMO channel features. In particular, we first examine the mmWave MIMO channel features in terms of azimuth angle of arrival (AAoA), elevation angle of arrival (EAoA), and path gain, and then extract these fine-grained channel features through the maximum-likelihood (ML) estimation method. Based on the extracted feature parameters, authentication validation is cast in the framework of hypothesis testing theory. We also derive the analytical expressions for the typical false alarm and detection rates by using the likelihood ratio test and thus the statistical performance is analytically established. Finally, extensive numerical results are provided to demonstrate the performance of the proposed authentication scheme.

Location Verification for Future Wireless Vehicular Networks: Research Directions and Challenges

Vehicle location information obtained through the global navigation satellite system (GNSS) will play a pivotal role in emerging vehicular networks. This vital information is, however, susceptible to a host of unwanted manipulations, especially if a malicious entity is involved. The most obvious example of such manipulations is the forwarding by a malicious vehicle of false GNSS locations to other members of the network. Such events can lead to poor operational outcomes for the vehicular network, and in extreme cases even lead to catastrophic safety violations. Here, we highlight research efforts pursued in the past few years that have attempted to address this weakness in vehicular networks. We also discuss the importance of location verification in the wake of emerging wireless technologies, such as those being proposed for beyond 5G (B5G) wireless vehicular networks. In particular, we detail an opportunity to conduct location reporting and verification simultaneously with the aid of mmWave technology and discuss how emerging machine learning (ML) techniques will provide for location verification solutions where reliability levels will be commensurate with that required by the vehicular network paradigm. We close by discussing the potential enhancements for location verification within a future combined B5G-ML architecture.

Incentivizing WiFi-Based Multilateration Location Verification

Due to the proliferation of WiFi devices and the high verification precision, researchers have shown interests in WiFi-based multilateration location verification (WMLV), where multiple WiFi APs (also known as verifiers) verify the location information claimed by a prover. However, it is a high expenditure for any single location-based service provider to deploy densely covered WiFi facilities. Incentivizing independent WiFi owners to corporately verify location information is thus a feasible solution to this plight, yet none of the previous research has taken this into consideration. To this point, we design a double auction-based incentive mechanism for WMLV, which motivates the participation of both provers and verifiers. More importantly, we consider practical situations, where the provers have various verification precision requirements, and different number of verifiers are required by different provers. The proposed double auction mechanism achieves desirable economical properties, including <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">truthfulness, individual rationality, computational efficiency, budget balance,</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nonnegative social welfare.</i> The desired properties are validated through both theoretical analysis and extensive simulations.

LoRa Positioning in Verification of Location Data’s Credibility

The LoRa is a novel radio communication technology providing low power and a high range of data transmission. The LoRa transmission may be used for a low-cost localization to estimate the network nodes’ location. Some recent research showed that the location could be found with reasonable accuracy, with median error as low as tens of meters. Still, such results are achieved in a controlled environment with low interferences. We first evaluate the LoRa localization using an extensive data set of a telemetric network of a few thousand devices. We show that although the direct positioning based on trilateration provides limited accuracy, the measurement of LoRa transmission may be successfully used to evaluate the credibility of location information. The information about which gateways received the data and the RSSI measurements allow us to verify if the potential coordinates of a location are accurate. We propose a metric for location verification and estimate its credibility on a sample of measurements from the LoRa telemetry network.

Verification of Dam Spatial Location in Open Datasets Based on Geographic Knowledge and Deep Learning

Dams are important water conservation hubs; however, dam locations provided by open datasets are often unreliable. The aim of this study is to provide a single, geographic location-reliable dataset of dams for the scientific community by fusing existing dam datasets and verifying these locations. Using Southeast Asia as the case study, we propose an efficient and automatic method to verify dam locations and developed a process framework. First, the possible location of a dam was obtained by analyzing its geographic location characteristics. Then, the deep learning method was used to detect dams. Finally, a variety of geographic knowledge was applied to comprehensively verify the detection results to obtain accurate and reliable dam location information. The fused dam dataset we produced includes the locations of 4,493 dams in Southeast Asia, which were verified using the proposed framework. The verification results were then evaluated via manual visual inspection. The verification accuracy of the framework was 86.7&#x0025;. The experimental results show that the proposed framework can quickly and reliably verify dam spatial locations and provide solutions for the spatial location verification of other remote sensing objects.

Who are there: Discover Twitter users and tweets for target area using mention relationship strength and local tweet ratio

The goal of Twitter users and tweets discovery for the target area is to find resident users and tweets created in the area. Previous works usually utilize following relationships among users to discover new users, whose precision and speed are easily affected by fake followers and the rate-limiting of getting Twitter data. In this manuscript, an algorithm for users and tweets discovery based on mention relationship strength and local tweet ratio (MRS-LTR for short) is proposed. First, the initial tweets are obtained by using Twitter API, from which the seed users and the seed tweets are extracted, and the mention relationship graph is constructed. Then, two sound-reasonable hypotheses about the location information contained in mention relationship and user’s tweets are explored and verified on the public dataset. Next, based on the two hypotheses, three new recommendation indicators are proposed to recommend new users in the area from the users mentioned by seed users. Finally, the candidate user location verification method based on profile and iteration probability is used to expand the seed user set. The principle analysis of discovery precision, discovery speed and algorithm function shows the effectiveness of MRS-LTR. Experimental results on a dataset of 4.4M users and 112M tweets demonstrate that MRS-LTR outperforms the state-of-the-art algorithm. The precision infimum and modified precision are increased by 14.79% and 31.22%, respectively. Meanwhile, the discovery speed is significantly improved, and the number of users discovered in the same period is about 8.7 times that of the existing algorithm.

Blockchain-Enabled Privacy-Preserving Location Sharing Scheme for LBSNs

The rise of Internet of Things (IoT) technology promotes the rapid development of location services industry. The idea of smart connectivity also provides a new direction for Location-Based Social Networks (LBSNs). However, due to limited calculate ability and internal storage space of IoT devices, historical location data of users is generally stored in the central server, which is likely to cause the disclosure of users’ private data. In this paper, we propose a Blockchain-enabled Privacy-Preserving Location Sharing (B-PPLS) scheme, which is a new framework that not only protects user location privacy but also provides effective location sharing services for users. For B-PPLS, location data owners can share the location area instead of location coordinates to Requesters, in order to realize the location privacy preserving. Also, the Merkle hash tree is utilized to divide the location area, so as to realize the multilevel privacy preserving. Furthermore, four algorithms are proposed to achieve the four stages of initialization, location record, location sharing, and location verification, respectively. Finally, we analyze the security of the proposed B-PPLS scheme and compare the performance with other related location privacy-preserving schemes by experimental evaluation.

Advance Attendance Calculation for Employees using QR Scan and Location Verification

In this era of technology smartphones play a significant role in our day to day life . The popularity of smartphones has increased over the years. This could change and speed up the attendance taking process in society. The current attendance system is time consuming and requires lots of manual workload[19]. This paper discusses the development of the Attendance calculation system for everyone in society whether schools, hotels, gyms, pubs, etc. This is all in one solution for measuring attendance, then salary and solving many problems in the proposed work. The proposed system is a couple of two applications, one for managing the staff details and the second application for taking the attendance by making employees account and giving access to mark their attendance individually. Every organisation whether government or private uses an information system[2.] to store data of their staff. However, in India it is found that many small scale industries use pen and paper to keep a record. However, there are many advanced technology systems available that can do this work but they all are costly for these low level industries. This paper discusses making a system for solving problems for them at a cheaper cost. This system will mark attendance of each employee and calculate the salary of them at the end of month. It also calculates overtime and total working hours of each employee. As in small scale each company has their own holidays preference and variable week off for employees, so all this power is given to the employer to manage holidays and week days of each employee separately. It saves lots of time and has no error in pay calculation hence preventing clashes between HR Team and employees. So that both employer and employee can focus on their work to develop their company.

ReliableBox: Secure and Verifiable Cloud Storage With Location-Aware Backup

While the prevalent cloud storage platforms are offering convenient services in support of diverse data-driven applications for clients, various security concerns raise in terms of data confidentiality, availability, and retrievability. Among them, servers' dishonesty on the location-specific data backup becomes a serious concern when the data stands out clients' control, considering the strict regulations imposed by many governments and organizations on data storage location. This article studies location-aware data backup verification for the data stored in clouds and aims to design a secure framework, named as ReliableBox, enabling the clients to verify if their data have been backed up on the remote servers with specific geolocation. In the design of ReliableBox, we leverage the prominent proof-of-storage techniques for data possession proof, and take advantage of multilateration geolocation and Intel SGX for the precise communication delay measurement and trust computing delay measurement, respectively. In ReliableBox, a client first computes integrity tags for the files and then outsources both the files and tags to the cloud storage server. In the later attestation, with the precise network delay and distance measurement from location-known verifiers, the client verifies that the outsourced files are intact and backed-up to hosts at the specific geolocation. With the customized design, ReliableBox can support the security needs in terms of both data integrity and backup location verification for clients, even when there exists potential dishonest cloud service providers who may manipulate the network delays or forge verification proofs. We provide security analysis to show the security property of ReliableBox in terms of data access, confidentiality, and verifications. In the end, we implement the system prototype and deploy it into several prevalent and commercial cloud platforms for performance evaluation. The experimental results demonstrate that ReliableBox is secure in support of data integrity checking and location-aware backup auditing, while it is robust to the data possession and location spoofing attacks.

AEALV: Accurate and Efficient Aircraft Location Verification for ADS-B

The automatic dependent surveillance-broadcast (ADS-B) collects and analyzes massive cognitive aircraft location data, and is essential for safe air traffic controls in the modern aviation industry. In this paper, we present an accurate and efficient aircraft location verification scheme (AEALV) that preserves aircraft location privacy by utilizing grid-based <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> -nearest neighbor ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> NN) algorithms. Specifically, we introduce a new approach to efficiently find the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> nearest grid squares in the ciphertext domain by leveraging vector homomorphic encryption. Further, we present a quick identification technique for aircraft legitimacy by validating claimed locations instead of estimating real locations of the aircraft. This validation only involves calculating encrypted Euclidean distances in a small training circle, thereby significantly saving the verification time of claimed locations. We conduct extensive experiments and evaluate AEALV using real-world data. The results show that AEALV achieves accurate and efficient verification for aircraft locations while maintaining the confidentiality of both aircraft locations and grid data.

Financial Security Analysis of E‐Commerce Platform Based on Supply Chain for Heterogeneous Network Location Verification

The development of supply chain finance, its pricing strategy for bilateral business cooperation between e‐commerce, banking institutions, and fourth‐party logistics services providers has gained the attention of researchers. This paper combines the heterogeneous network location verification technology, starting from information asymmetry and Rubens bargaining game ideas, and combines it with game theory methods to provide a reference for bilateral cooperation decision‐making on e‐commerce platforms. The experiment results indicated the pricing decisions of e‐commerce platforms which are affected by the efforts of the other party and the ability of bargaining. The quoted price increases with the decrease of the bank’s ability and the increase of the service provider’s ability. The pricing decisions of banks and service providers are only affected by the direct proportion of their respective business costs. When considering the introduction of incentive mechanism conditions, it is found that appropriate incentive conditions can increase the quotation of the e‐commerce platform. The price quoted by the e‐commerce platform that chooses to bargain with the bank is higher than the price quoted by the bank after negotiating with the service provider, which will help to better realize the benefits. Finally, the paper numerically analyzes the results of the bargaining game between e‐commerce platforms and banks and fourth‐party logistics service providers, and the numerical results verify the better performance.

Classification of Location Verification in WSNs

Classification of Location Verification in WSNs

A More General Information Theoretic Study of Wireless Location Verification System

In recent years, the issue of location verification system (LVS) has received significant attention in wireless communication networks, especially in Intelligent Transportation Systems (ITSs) and vehicular technology, where the location information is important for users' safety and security. In this article, we consider a vehicular network and analyze a more general Information Theoretic (ITc) study of LVS, specifically, aiming at finding an optimum decision threshold for detection a spoofed location and increasing the system ability in correctly detecting malicious users. For this purpose, we propose different information theoretic measures (Renyi divergence, Renyi mutual information, Kullback-Leibler divergence, Kullback-Leibler mutual information and Jensen-Shannon divergence) for identifying malicious user. Simulation results and comparison between proposed measures show that the optimum decision threshold can be achieved by applying the Jensen-Shannon divergence measure.

A Decentralized Location Privacy-Preserving Spatial Crowdsourcing for Internet of Vehicles

With the rapid development of Internet of Vehicles (IoV), vehicle-based spatial crowdsourcing (SC) applications have been proposed and widely applied to various fields. However, location privacy leakage is a serious issue in spatial crowdsourcing because workers who participate in a crowdsourcing task are required to upload their driving locations. In this paper, we propose a decentralized location privacy-preserving SC for IoV, which allows vehicle users to securely participate in SC with ensuring the task’s location policy privacy and providing multi-level privacy preservation for workers’ locations. Specifically, we introduce blockchain technology into SC, which can eliminate the control of vehicle user data by SC-server. We combine the additively homomorphic encryption and circle-based location verification to ensure the confidentiality of task’s location policy. To achieve multi-level privacy preservation for workers’ driving locations, we only reveal a grid where workers are located in. The size of the grid represents the level of privacy preservation. We leverage the order-preserving encryption and non-interactive zero-knowledge proof to prevent workers from illegally obtaining rewards by forging their driving locations. The security analysis results show that our framework can satisfy the above requirements. In addition, the experiment results demonstrate that our framework is efficient and feasible in practice.

Enabling Location Privacy Preservation in MANETs Based on Distance, Angle, and Spatial Cloaking

Preserving the location privacy of users in Mobile Ad hoc Networks (MANETs) is a significant challenge for location information. Most of the conventional Location Privacy Preservation (LPP) methods protect the privacy of the user while sacrificing the capability of retrieval on the server-side, that is, legitimate devices except the user itself cannot retrieve the location in most cases. On the other hand, applications such as geographic routing and location verification require the retrievability of locations on the access point, the base station, or a trusted server. Besides, with the development of networking technology such as caching technology, it is expected that more and more distributed location-based services will be deployed, which results in the risk of leaking location information in the wireless channel. Therefore, preserving location privacy in wireless channels without losing the retrievability of the real location is essential. In this paper, by focusing on the wireless channel, we propose a novel LPP enabled by distance (ranging result), angle, and the idea of spatial cloaking (DSC-LPP) to preserve location privacy in MANETs. DSC-LPP runs without the trusted third party nor the traditional cryptography tools in the line-of-sight environment, and it is suitable for MANETs such as the Internet of Things, even when the communication and computation capabilities of users are limited. Qualitative evaluation indicates that DSC-LPP can reduce the communication overhead when compared with k-anonymity, and the computation overhead of DSC-LPP is limited when compared with conventional cryptography. Meanwhile, the retrievability of DSC-LPP is higher than that of k-anonymity and differential privacy. Simulation results show that with the proper design of spatial divisions and parameters, other legitimate devices in a MANET can correctly retrieve the location of users with a high probability when adopting DSC-LPP.