Location Privacy Issues Research Articles

RESEARCH ON GEOGRAPHIC INFORMATION DATA CIRCULATION SUPPORTS THE CONSTRUCTION OF DIGITAL CHINA

Abstract. Accelerating digital development and building a Digital China is an important engine for promoting Chinese-style modernization, which has become the development tone of the 14th Five-Year Plan period. The Overall Layout Plan for the Construction of Digital China clearly requires the improvement of the three-dimensional "One Map" of natural resources and the basic information platform of land and space, and the transformation of governance mode driven by digitalization. Giving full play to the value of geographic information data elements and comprehensively and effectively supporting national and local high-quality development has become an important topic for the construction of Digital China. Firstly, this paper expounds the connotation and application of geographic information data circulation. Secondly, it proposed the construction of geographic information data circulation system, designed the overall framework, basic requirements and technical indicators of geographic information data infrastructure and geographic information resource database, in order to better support the construction of Digital China. Thirdly, other aspects of geographic information data circulation that need to be paid attention to are given. It mainly includes geographic information data security and user location privacy issues aspects. Finally, the opportunities and challenges of geographic information data circulation are summarized.

A Sensitivity-based Location Privacy Protection Scheme in Vehicular Networks

In recent times, the issue of vehicle location privacy has received increasing attention. Location-based services (LBSs) require users’ location information to be constantly updated to service providers, which causes the location information to be speculated and attacked by malicious entities. The pseudonym schemes offer a viable solution to the aforementioned problem, but existing pseudonym schemes do not provide differentiated protection for users’ varying locations, thereby increasing the possibility of location information leakage. To address this concern, we propose a sensitivity-based pseudonym exchange mechanism, which leverages the vehicle’s historical track record to extract features and enable customized location privacy protection. Performance evaluation results demonstrate that our approach significantly outperforms existing approaches in achieving location privacy.

Location Privacy-Aware and Energy-Efficient Offloading for Distributed Edge Computing

Driven by the ever-increasing scale and intensity of the computing tasks arising from various mobile applications, distributed edge computing has fostered wide research interests. It can effectively reduce the task processing delay by partitioning the original large-scale task into several small subtasks and offloading them to multiple edge nodes (ENs) for parallel computing. In edge computing, as the mobile user usually tends to offload computing tasks to closer ENs to save transmit power, the attacker may stealthily infer user location by exploiting this feature. Although there have been some pioneering works on offloading related location privacy, they mainly focused on the scenario where each task can only be offloaded to a single EN, and may not be directly applicable to distributed edge computing. Besides, the privacy issues considered in existing works are mainly based on good heuristics, and there is still a lack of concrete examples of location privacy attacks in edge computing. To the best of our knowledge, the location privacy issue in distributed edge computing still remains largely unexplored in existing literature. With this consideration, a location inference attack based on matrix sequential probability ratio test (MSPRT) is identified in this work. Besides, a countermeasure based on dynamic multi-EN selection is proposed, together with a location privacy-aware and energy-efficient distributed offloading scheme based on the generic Lyapunov optimization framework. Both theoretic analysis and simulations based on real-world channel measurements are employed to validate the feasibility of the identified MPSRT attack and the effectiveness of the proposed defense scheme.

Security Compliant and Cooperative Pseudonyms Swapping for Location Privacy Preservation in VANETs

Vehicular Ad-hoc Networks (VANETs) are an essential part of the Intelligent Transportation System (ITS). VANETs promise to offer drivers and passengers safety, traffic efficiency, and infotainment services. To this end, VANETs require vehicles to periodically broadcast unencrypted beacon messages that contain position, velocity, timestamp, and identity. Consequently, VANETs naturally expose users' identities and locations, facilitating tracking by an adversary. To address VANETs location privacy issues, researchers have proposed different pseudonym-based privacy-preserving techniques in the literature. One of the techniques is pseudonym swap. In pseudonym swap techniques, vehicles exchange pseudonyms with each other hence fewer pseudonyms for each vehicle since vehicles can reuse pseudonyms. However, existing pseudonym swap-based schemes either exchange pseudonyms via Roadside Units (RSUs) or ignore VANETs' security requirements by not informing the Certification Authority of the exchanges. Hence, they suffer from high RSUs deployment costs, inflexibility to location privacy preservation, unaccountability, and repudiation attacks. Moreover, the lower level of location privacy provided by the schemes demands further improvements. Therefore, we propose a novel pseudonym swap scheme that uses swappable and non-swappable pseudonyms to attain infrastructure independence while adhering to VANET security requirements. In this scheme, vehicles, independent of RSUs, cooperatively swap and change pseudonyms within mix-contexts having a high level of location privacy. Each exchange is then reported to the authority to enforce accountability and non-repudiation. Besides adhering to VANET security requirements, the simulation experiments show that our scheme outperforms existing pseudonym swap schemes, providing VANET users with a high level of location privacy.

DARVAN: A fully decentralized anonymous and reliable routing for VANets

With the recent advances in the automobile industry and the emergence of smart self-driving vehicles, the increasing need for proper and optimal Vehicular Ad-hoc Network (VANet) protocols has drawn the attention of many researchers. Nevertheless, the VANet community has yet to agree on a unified and suitable set of protocols for such networks. One of the major concerns in these networks is security considerations, which are significant because of the specific characteristics of VANet. In this article, we aim to address the location privacy and reliability issues in VANet routing protocols. We propose DARVAN, a fully decentralized infrastructure that provides anonymous and reliable routing in VANets. By employing a distributed database and collective consensus, DARVAN minimizes the exposure of critical data, which is conventionally stored and processed in centralized units. DARVAN is deployed by modifying the I2P protocol, which aims to improve routing reliability and resilience to many adversary activities in VANets. Specifically, with DARVAN, we present an effective and efficient network-level mitigation for Sybil attacks in Vanets. Our extensive simulations on NS3 show that DARVAN performs well in terms of packet delivery ratio, overhead, delay, and reliability compared to the previous anonymous scheme proposed for VANet routing.

A Novel Deception-Based Scheme to Secure the Location Information for IoBT Entities

The Internet of Battlefield Things (IoBT) is a subdomain of the Internet of Things (IoT) that applies to a battlefield. Due to the limited communication infrastructure, the entities in the battlefield communicate with each other using Device-2-Device (D2D) communication. D2D is used to send and request extremely sensitive data as well as the location information of other entities. Thus, to execute tasks and avoid information leaks, it is critical to protect the location data of soldiers and equipment. To the best of our knowledge, location privacy issues in IoBT have not been fully investigated. The attackers are expected to probe the network before attacking valuable assets, giving them an advantage over the defenders. This advantage can be offset by using deception-based methods. In this study, we propose a deception-based scheme to enhance the location information security of IoBT nodes. A novel encryption method is proposed along with dummy identities and dummy packets. We develop a mathematical model to evaluate the proposed scheme in terms of safety time, probability of failure, and probability of identifying the real packet. The obtained results demonstrate that the proposed method is efficient in reducing the probability of identifying the real location of the communicating entities. We develop NetLogo simulation to validate the mathematical model.

“Do You Know You Are Tracked by Photos That You Didn’t Take”: Large-Scale Location-Aware Multi-Party Image Privacy Protection

Most existing image privacy protection works focus mainly on the privacy of photo owners and their friends, but lack the consideration of other people who are in the background of the photos and the related location privacy issues. In fact, when a person is in the background of someone else’s photos, he/she may be unintentionally exposed to the public when the photo owner shares the photo online. Not only a single visited place could be exposed, attackers may also be able to piece together a person’s travel route from images. In this article, we propose a novel image privacy protection system, called LAMP, which aims to light up the location awareness for people during online image sharing. The LAMP system is based on a newly designed location-aware multi-party image access control model. Unlike previous works on small scales, the LAMP system is highly efficient and scalable as it can enforce privacy protection for billions of users on social networks in real time. The LAMP system automatically detects the user’s occurrences on photos regardless the user is the photo owner or not. Once a user is identified and the location of the photo is deemed sensitive according to the user’s privacy policy, the user’s face will be replaced with a synthetic face. A prototype of the system was implemented and evaluated to demonstrate its applicability in the real world.

Blockchain-Based Lightweight Multifactor Authentication for Cell-Free in Ultra-Dense 6G-Based (6-CMAS) Cellular Network

Cell-Free mMIMO is a part of technology that will be integrated with future 6G ultra-dense cellular networks to ensure unlimited wireless connectivity and ubiquitous latency-sensitive services. Cell-Free gained researchers’ interest as it offers ubiquitous communication with large bandwidth, high throughput, high data transmission, and greater signal gain. Cell-Free eliminates the idea of cell boundary in cellular communication that reduces frequent handover and inter-cell interference issues. However, the effectiveness of the current authentication protocol could become a serious issue due to the dynamic nature of Cell-Free in densely distributed, high number of users, high mobility, and frequent data exchange. Secondly, secure communication may be achieved in such a dynamic environment at the expense of high authentication overhead, high communication and computational costs. To address the above security challenges, we proposed a lightweight multifactor mutual authentication protocol for Cell-Free communication using ECC-based Deffie Hellman (ECDH). This scheme utilizes timestamping, one-way hash function, Blind-Fold Challenge scheme with public key infrastructure. The proposed cryptosystem integrates with blockchain technology using proof of staked (POS) as a consensus mechanism to ensure integrity, non-repudiation and traceability. The proposed scheme can enforce the mitigation of several major security attacks on communication links such as spoofing attacks, eavesdropping, user location privacy issues, replay attacks, denial of service attacks, and man-in-the-middle (MITM) attacks, which is one of the significant features of the scheme. Furthermore, this scheme contributes to reducing authentication, communication, and computational overheads with an average of 32.8%, 52.4% and 53.2% better performance respectively as compared baseline authentication protocols.

Indoor Passive Visual Positioning by CNN-Based Pedestrian Detection.

Indoor positioning applications are developing at a rapid pace; active visual positioning is one method that is applicable to mobile platforms. Other methods include Wi-Fi, CSI, and PDR approaches; however, their positioning accuracy usually cannot achieve the positioning performance of the active visual method. Active visual users, however, must take a photo to obtain location information, raising confidentiality and privacy issues. To address these concerns, we propose a solution for passive visual positioning based on pedestrian detection and projection transformation. This method consists of three steps: pretreatment, pedestrian detection, and pose estimation. Pretreatment includes camera calibration and camera installation. In pedestrian detection, features are extracted by deep convolutional neural networks using neighboring frame detection results and the map information as the region of interest attention model (RIAM). Pose estimation computes accurate localization results through projection transformation (PT). This system relies on security cameras installed in non-private areas so that pedestrians do not have to take photos. Experiments were conducted in a hall about 100 square meters in size, with 41 test-points for the localization experiment. The results show that the positioning error was 0.48 m (RMSE) and the 90% error was 0.73 m. Therefore, the proposed passive visual method delivers high positioning performance.

Improved Chaff-Based CMIX for Solving Location Privacy Issues in VANETs

Safety application systems in Vehicular Ad-hoc Networks (VANETs) require the dissemination of contextual information about the scale of neighbouring vehicles; therefore, ensuring security and privacy is of utmost importance. Vulnerabilities in the messages and the system’s infrastructure introduce the potential for attacks that lessen safety and weaken passengers’ privacy. The purpose of short-lived anonymous identities, called “pseudo-identities”, is to divide the trip into unlinkable short passages. Researchers have proposed changing pseudo-identities more frequently inside a pre-defined area, called a cryptographic mix-zone (CMIX) to ensure enhanced protection. According to ETSI ITS technical report recommendations, the researchers must consider the low-density scenarios to achieve unlinkability in CMIX. Recently, Christian et al. proposed a Chaff-based CMIX scheme that sends fake messages under the consideration of low-density conditions to enhance vehicles’ privacy and confuse attackers. To accomplish full unlinkability, in this paper, we first show the following security and privacy vulnerabilities in the Christian et al. scheme: Linkability attacks outside the CMIX may occur due to deterministic data sharing during the authentication phase (e.g., duplicate certificates for each communication). Adversaries may inject fake certificates, which breaks Cuckoo Filters’ (CFs) updates authenticity, and the injection may be deniable. CMIX symmetric key leakage outside the coverage may occur. We propose a VPKI-based protocol to mitigate these issues. First, we use a modified version of Wang et al.’s scheme to provide mutual authentication without revealing the real identity. To this end, the messages of a vehicle are signed with a different pseudo-identity “certificate”. Furthermore, the density is increased via the sending of fake messages in low traffic periods to provide unlinkability outside the mix-zone. Second, unlike Christian et al.’s scheme, we use the Adaptive Cuckoo Filter (ACF) instead of CF to overcome the false positives’ effect on the whole filter. Moreover, to prevent any alteration of the ACFs, only RUSs distribute the updates, and they sign the new fingerprints. Third, the mutual authentication prevents any leakage from the mix zones’ symmetric keys by generating a fresh one for each communication through a Diffie–Hellman key exchange.

Security Challenges of Location Privacy in VANETs and State-of-the-Art Solutions: A Survey

A Vehicular Ad-hoc Network (VANET) comprises a group of moving or stationary vehicles connected by a wireless network. VANETs play a vital role in providing safety and comfort to drivers in vehicular environments. They provide smart traffic control and real-time information, event allocation. VANETs have received attention in support of safe driving, intelligent navigation, emergency and entertainment applications in vehicles. Nevertheless, these increasingly linked vehicles pose a range of new safety and security risks to both the host and its associated properties and may even have fatal consequences. Violations of national privacy and vehicle identities are a major obstacle to introducing forced contact protocols in vehicles. Location privacy refers to the privacy of the vehicle (driver) and the location of the vehicle. Whenever a vehicle sends a message, no one but authorized entities should know their real identity and location of the vehicle. All the messages sent by the vehicle must be authenticated before processing, hence location privacy is an important design aspect to be considered in VANETs operations. The novelty of this paper is that it specifically reviews location privacy in VANETs in terms of operational and safety concerns. Furthermore, it presents a critical analysis of various attacks, identity thefts, manipulation and other techniques in vogue for location privacy protection available in state-of-the-art solutions for VANETs. The efforts in this paper will help researchers to develop a great breadth of understanding pertaining to location privacy issues and various security threats encountered by VANETs and present the critical analysis of the available state-of-the- art solutions to maintain location privacy in VANETs.

Personalized Location Privacy Protection Based on Vehicle Movement Regularity in Vehicular Networks

Currently, the issue of location privacy has been attracting increasing attention. In traditional pseudonym-based solutions, the pseudonym age of each vehicle increases at the same rate and vehicles change their pseudonyms when they meet vehicles, which also want to change pseudonyms at the same location. However, we observe that according to the individual characteristics of each vehicle’s owner, the movement of each vehicle is regular, i.e., there are some locations that vehicles visit frequently, and some locations that they rarely visit. Evidently, pseudonym change strategies for these locations are different. To address these issues, we propose a sensitivity-based pseudonym change mechanism, which can take full advantage of the regularity of a vehicle’s movement, thereby achieving personalized location privacy. A pseudonym age analytical model is used to quantify the achieved location privacy. Furthermore, we propose a new metric to measure the level of personalized location privacy protection. The results of the performance evaluation demonstrate that our approach significantly outperforms existing approaches in realizing personalized location privacy.

Privacy-Aware Autonomous Valet Parking: Towards Experience Driven Approach

Driverless parking, an influential application of Mobility as a Service (MaaS) model, is one of the clear early benefits for autonomous vehicles, given often narrow spaces and multiple potential hazards (such as pedestrians stepping out from in between other vehicles). In recent years, real momentum has been building up for designing automated parking models for vehicles. However, in such an autonomous parking design, location privacy and identity privacy issues are always overlapping due to the improper sharing of data. Most existing studies barely investigate and poorly address such privacy issues. Motivated by this, we develop (and evaluate) an experience-driven, secure and privacy-aware framework of parking reservations for automated cars. Our idea of using differential privacy with zero-knowledge proof provides both security and privacy guarantees to users. Furthermore, the performance of the developed model is enhanced by exploiting reinforcement learning approach such that the utility of the system and the parking reservation rate can be maximized. Extensive evaluation demonstrates the superiority of the proposed model.

Finding Location of Fake and Phantom Source for Source Location Privacy in Wireless Sensor Network

Wireless sensor network (WSN) has various advantages over wired networks and hence it has been an important area of research since years. Several issues of WSN such as energy, delay, routing, security have been addressed rigorously in the past researches. However, the issues of privacy is still grey zoned. In privacy, the issue of source location privacy (SLP) is critical for targeting and monitoring applications. Past works addressing the issue of SLP have used variants of fake and phantom sources, establishing the use of fake and phantom sources as a promising resource for SLP. Amid these, the selection of proper location of fake and phantom sources is still an issue which significantly impacts the privacy as well as other constraints of WSN. To fill this gap, we propose an algorithm to select the location of fake and phantom sources in the network in order to provide better privacy and energy trade-off in the network. Our approach uses the concept of geometry for selecting the location of fake and phantom sources. The experimental results confirm that the proposed algorithm gives a promising trade-off between privacy and other constraints such as energy and traffic.

Preserving Location Privacy in UASN through Collaboration and Semantic Encapsulation

UASN has gained much attention during the past decades. Based on fruitful research and development, UASN could be a compelling component of the global IoT in the future. From the view of location privacy preservation, state-of-the-art investigations mainly concentrate on UASN infrastructures and focus on the location privacy preserving of the source location privacy of the infrastructures, such as anchor nodes, positioning nodes, and communication channel security. However, in the IoT scenario, the underwater positioning service provided by UASN becomes pervasive, and the location privacy of the end-user of this service will come into sight. On the other hand, the presentation model of underwater semantic location is still in use of traditional terrestrial coordinates and geometric points, the concept of underwater semantic location could be further adopted to improve the location privacy. In this article, both these two location privacy issues are concerned. From the view of a UASN positioning service user, we discuss location privacy-related problems and propos a UASN nodes collaboration method to prevent the adversary from learning the precise location of the user when they intercept the communication or hack the central node. Then a concept of "semantic envelope" is designed to encapsulate the location information, which could further strengthen the location privacy.

Protecting Geolocation Privacy of Photo Collections

People increasingly share personal information, including their photos and photo collections, on social media. This information, however, can compromise individual privacy, particularly as social media platforms use it to infer detailed models of user behavior, including tracking their location. We consider the specific issue of location privacy as potentially revealed by posting photo collections, which facilitate accurate geolocation with the help of deep learning methods even in the absence of geotags. One means to limit associated inadvertent geolocation privacy disclosure is by carefully pruning select photos from photo collections before these are posted publicly. We study this problem formally as a combinatorial optimization problem in the context of geolocation prediction facilitated by deep learning. We first demonstrate the complexity both by showing that a natural greedy algorithm can be arbitrarily bad and by proving that the problem is NP-Hard. We then exhibit an important tractable special case, as well as a more general approach based on mixed-integer linear programming. Through extensive experiments on real photo collections, we demonstrate that our approaches are indeed highly effective at preserving geolocation privacy.

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.

Toward Addressing Location Privacy Issues: New Affiliations with Social and Location Attributes

Nowadays, location-sharing applications (LSA) within social media enable users to share their location information at different levels of precision. Users on their side are willing to disclose this kind of information in order to represent themselves in a socially acceptable online way. However, they express privacy concerns regarding potential malware location-sharing applications, since users’ geolocation information can provide affiliations with their social identity attributes that enable the specification of their behavioral normativity, leading to sensitive information disclosure and privacy leaks. This paper, after a systematic review on previous social and privacy location research, explores the overlapping of these fields in identifying users’ social attributes through examining location attributes while online, and proposes a targeted set of location privacy attributes related to users’ socio-spatial characteristics within social media.

QBPP: Quality of services–based location privacy protection method for location-based services in cloud-enabled Internet of vehicles

Location-based services has been widely applied in cloud-enabled Internet of vehicles. Within these services, location privacy issues have captured significant attention. Vehicles use the technology of anonymity to implement occultation, the location is not revealed. In this process, large-scale data transmissions can reduce the quality of services. In order to ensure location privacy and high-quality services, the cloud manager customizes virtual machines for vehicles to support location-based services according to the vehicles’ demands. To achieve better performance, this article presents a conditional anonymity method that does not use bilinear pairings to address the problem of privacy disclosure by using discrete logarithm problem and Diffie–Hellman problem. Moreover, asymmetric key algorithms are used in the Internet of vehicles environment to reduce the cost. To guarantee secure data transmission in Internet of vehicles, the batch validation technique is used to address data integrity. Our theoretical security analysis and experiments show that the proposed scheme is secure in compared attack models, such as impersonation attacks, replay attacks, the man-in-the-middle attacks, and so on. Our proposed scheme ensures the security requirements such as message authentication, location privacy protection, and traceability, while lowering transmission and computation cost.

Achieving Differentially Private Location Privacy in Edge-Assistant Connected Vehicles

Connected vehicles can provide safer and more satisfying services for drivers by using information sensing and sharing. However, current network architecture cannot support massive and real-time data transmissions due to the poor-quality wireless links. To provide real-time data processing and improve drivers' security, edge computing is regarded as a promising method to offer more efficient services by placing computing and storage resources at the network edge. In this paper, we will introduce the concept of edge-assistant connected vehicles and propose some promising applications to reduce the network traffic and provide real-time services with the help of massive edge nodes. Unlike in the traditional cloud-based connected vehicles, edge nodes are introduced to enable vehicles to obtain real-time and distributed processing services. Furthermore, considering the location privacy issue in the new architecture, we propose a novel differentially privacy-preserving location-based service usage framework deployed on the edge node, designed to provide an adjustable privacy protection solution to balance the utility and privacy. Finally, we conduct extensive experiments to verify the proposed framework.