Location Privacy Of Mobile Users Research Articles

Guest Editorial: Special Section on Distributed Intelligence Over Internet of Things

N OWADAYS, billions of devices are connected to the In-ternet, enabling Internet of Things (IoT) systems widely deployed, such as smart city, smart healthcare and intelligent plant, to capture a great quantity of sensing data. Consequently, the data transmission, processing and analysis in IoT applications bring a great pressure to the central server. Fortunately, distributed intelligence becomes one of the potential solutions. Distributed intelligence can greatly relieve server pressures via plenty of terminal devices, and these devices collaboratively perceive and handle the mass data to improve the reliability, s-calability and security of industrial IoT systems. As future IoT system will embrace more wireless sensors and devices, the high-performance computing, high-bandwidth and low-latency communication are excessively required, many new research opportunities and challenges for distributed intelligence over Internet of things have arisen. To promote the development of distributed intelligence technology, this special section (SS) focuses on various technologies and platforms regarding industrial IoT systems. This special section received nearly 50 submitted manuscripts, out of which 10 of them have been accepted after a rigorous peer review. Each manuscript is reviewed by multiple rounds of review with at least three or four reviewers, the problems to be solved and the innovation of each manuscript are mainly concerned. Then the accepted papers are summarized as follows in details. Considering the joint optimization of the offloading decision and resource allocation under limited resource constraints in collaborative edge computing networks with multiple IIoT devices and MEC servers, an improved differential evolution algorithm [7] is proposed to minimize the weighted sum of cost of energy consumption and time delay, which can effectively reduce the system delay and energy consumption. In order to improve the performance of task scheduling in cloud computing, Attiya et al. [1] propose a novel hybrid swarm intelligence method MRFOSSA, which uses a modified Manta-Ray Foraging Optimizer (MRFO) and the Salp Swarm Algorithm (SSA). MRFOSSA is superior to other methods in terms of makespan time and cloud throughput. The research goal of the paper [5] is to design an intelligent computing offloading strategy for industrial applications in order to optimize costs and mitigate energy losses. Then the paper proposes to combine a fog controller and AI-based learning techniques so that the fog controller can intelligently assign tasks to the most appropriate fog devices and find the appropriate path to the target. Considering the resource utilization efficiency under dynamic overload requests and network states in IIoT, Chen et al. [2] propose DRL-based intelligent SFC orchestration scheme and jointly optimize the VNF deployment and SFC embedment by the improved DDQN algorithm, which can improve the performance of resource utilization rate, execution cost and delay compared with other representative schemes. To solve the problem of resource allocation and energy cost in Internet of Vehicles, Kong et al. [8] design a joint computing and caching framework and formulate the problem as a reinforcement learning problem to minimize the energy cost. On this basis, the optimization algorithm based on DDPG is proposed, which can effectively decrease energy costs. To reduce the query numbers of the object model when constructing adversarial examples, Zhang et al. [10] propose generating adversarial examples with shadow model (GASM), i.e., transfering the query operations to the designed shadow model, which can achieve high attack success rates. Chen et al. [3] revise a Decentralized-Wireless-Federated-Learning algorithm (DWFL) which utilizes the superposition property of the analog scheme. It can solve the problem of single failure, limited bandwidth resource and privacy protection in wireless federated learning algorithm, which can be applied widely in wireless IoT networks. To reduce the resource consumption in CNN-based applications, Jia et al. [6] propose the CNN-based Resource Optimization APProach which utilizes model compression and computation sharing to optimize inner-model and inter-model respectively, and the comparison results show the superior performance in scalability and the decrease of resource cost. In mobile crowdsensing activities, Gao et al. [4] propose a differential Location Privacy-preserving Mechanism based on Trajectory obfuscation (LPMT) to protect the location privacy of mobile users, which includes three operations: stay points extraction, stay points obfuscation and stay points sampling. In order to mimic the task-free bottom-up visual attention process by predicting salient regions on natural images, Umer et al. [9] propose a Pseudo Knowledge Distillation (PKD) model based on knowledge distillation and pseudo labelling technique, which is computationally efficient and suitable for real-time on-device saliency prediction.

A Lightweight Privacy-Preserving Fair Meeting Location Determination Scheme

Equipped with mobile devices, people relied on location-based services (LBSs) can expediently and reasonably organize their activities. But location information may disclose people’s sensitive information, such as interests and health status. Besides, the limited resources of mobile devices restrict the further development of LBSs. In this article, aiming at the fair meeting position determination service, we design a lightweight privacy-preserving solution. In our scheme, mobile users only need to submit service requests. A cloud server and a location services provider are responsible for service response, where the cloud server achieves most of the calculation, and the location services provider determines the fair meeting location based on the computational results of the cloud server and broadcasts it to mobile users. The proposed scheme adopts homomorphic encryptions and random permutation methods to preserve the location privacy of mobile users. The security analyses show that the proposed scheme is privacy preserving under our defined threat models. Besides, the presented solution only needs to calculate $n$ Euclidean distances, and hence, our scheme has linear computation and communication complexity.

An Efficient Approach for Privacy-Preserving of the Client’s Location and Query in M-Business Supplying LBS Services

Privacy-preserving in mobile business supplying location-based services (LBS) has the potential to become a primary concern for clients and service providers. In m-business providing LBS services, a client sends its exact locations to service providers. This data may involve sensitive and private personal information. Therefore, the misuse of location information by service providers creates privacy issues for clients. Moreover, the query must not be linked to the mobile client, even if the location information is exposed willingly by her/him to obtain specific services. Thus, there are location cloaking algorithms that allow the protection of the location privacy of mobile users. Hence, many temporal and spatial approaches to cloaking a specific user’s location have been proposed. Different from the existing methods, the current works define location and query privacy separately. Therefore, in this paper, we investigate the issues related to the mobile client privacy. Mainly, we aim to preserve the client location privacy as well as the continuous queries privacy, where mobile clients continuously emit different queries during their travels. It’s on this premise that we propose a new clique-based cloaking algorithm named Mobile Clique Cloak (MCC) to preserve the mobile client’s privacy in the M-business providing LBS services. Also, to build the cloaking region in our approach, we take into account the similarity of client velocity and direction to obtain a right balance between quality of service QoS and privacy. Furthermore, we generate different realistic dummies instead of dropping the query; thus, all queries will be processed even in the case of k−1 other mobile clients’ queries cannot be found. Moreover, our work deals with a series of attacks in the same cloaking process (location attack, tracking attack, query sampling attacks and homogenous attack). We evaluate our approach from three aspects: privacy guaranty, quality of service and performance. Experimental evaluation of our algorithms on a real world map shows that our approach ensures total privacy for clients and protects the privacy of clients during the entire query period whiles allowing clients’ choice of privacy requirements. Besides, we compare our algorithm with existing privacy protection algorithms such as V-DCA, D-TC and GCA. According to the evaluation results and a comparison of the algorithms, our algorithm MCC can make a good balance between quality of service, performance and privacy.

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.

A Novel Method for Location Privacy Protection in LBS Applications

Location-based services have become a mainstream in people’s daily lives due to continuous innovations in the field of mobile networking and GPS technologies. Recently they have advanced into a hot topic to which the majority of researchers pay close attention about how to enjoy them while safeguarding the location privacy of mobile users. Existing works involve the injection of random noise that cannot pledge the quality of service. Herein this manuscript, we propose a novel location privacy protection model based on the loss of service quality. This model allows the user to express his/her requirement of service quality by specifying the maximum service quality loss Lmax, which is the user’s tolerance. Lmax can be set to 0. Our comprehensive experimental evaluation using a real-world dataset demonstrates that our modus outdoes other state-of-the-art approaches.

STAMP: Enabling Location Privacy for Mobile Users While Using Public Wi-Fi

STAMP: Enabling Location Privacy for Mobile Users While Using Public Wi-Fi

Providing location-aware location privacy protection for mobile location-based services

Location privacy has been a serious concern for mobile users who use location-based services provided by third-party providers via mobile networks. Recently, there have been tremendous efforts on developing new anonymity or obfuscation techniques to protect location privacy of mobile users. Though effective in certain scenarios, these existing techniques usually assume that a user has a constant privacy requirement along spatial and/or temporal dimensions, which may be not true in real-life scenarios. In this paper, we introduce a new location privacy problem: Location-aware Location Privacy Protection (L2P2) problem, where users can define dynamic and diverse privacy requirements for different locations. The goal of the L2P2 problem is to find the smallest cloaking area for each location request so that diverse privacy requirements over spatial and/or temporal dimensions are satisfied for each user. In this paper, we formalize two versions of the L2P2 problem, and propose several efficient heuristics to provide such location-aware location privacy protection for mobile users. Through extensive simulations over large synthetic and real-life datasets, we confirm the effectiveness and efficiency of the proposed L2P2 algorithms.

Future Challenging Issues in Location based Services

The fast advances of mobile devices and positioning technologies has led to the flourish of Location-Based Services (LBS), in that people want to enjoy wireless services everywhere like in hotels, colleges, etc. LBS, the branch of computer program level services used in various fields and support, the application are broadly classified as Maps and Navigation, Information service, Tracking service, Social networking, Games, Vehicular navigation and Advertising etc. Now a days, LBSs attract millions of mobile users for example include POI finders such as Qype, which help the users to find the next POI such as bars or cinemas, and enrich the provided information. But during this communication, Security and Privacy of personal location information (of LBSs users) is becoming an increasingly important issue for future. So concerning it (privacy) as an important issue, discusses several privacy preserving location issues for vehicular (mobile) users (since knowledge of a vehicle‟s location can result in leakage of sensitive information). Location privacy for mobile users is mainly determined into two levels such as internally by a device or externally by systems and kind of networks with which the device interrelates. Users wish to maintain the vehicle„s information is known only to those legally authorized to have access to them and remain unknown to anybody unauthorized. Hence the purpose and contribution of this paper is to discussed about various privacy and challenges issues in LBSs increasing in future that have not been published in the any research journal so far.

Attack-Resilient Mix-zones over Road Networks: Architecture and Algorithms

Continuous exposure of location information, even with spatially cloaked resolution, may lead to breaches of location privacy due to statistics-based inference attacks. An alternative and complementary approach to spatial cloaking based location anonymization is to break the continuity of location exposure by introducing techniques, such as mix-zones, where no application can trace user movements. Several factors impact on the effectiveness of mix-zone approach, such as user population, mix-zone geometry, location sensing rate and spatial resolution, as well as spatial and temporal constraints on user movement patterns. However, most of the existing mix-zone proposals fail to provide effective mix-zone construction and placement algorithms that are resilient to timing and transition attacks. This paper presents MobiMix, a road network based mix-zone framework to protect location privacy of mobile users traveling on road networks. It makes three original contributions. First, we provide the formal analysis on the vulnerabilities of directly applying theoretical rectangle mix-zones to road networks in terms of anonymization effectiveness and resilience to timing and transition attacks. Second, we develop a suite of road network mix-zone construction methods that effectively consider the above mentioned factors to provide higher level of resilience to timing and transition attacks, and yield a specified lowerbound on the level of anonymity. Third, we present a set of mix-zone placement algorithms that identify the best set of road intersections for mix-zone placement considering the road network topology, user mobility patterns and road characteristics. We evaluate the MobiMix approach through extensive experiments conducted on traces produced by GTMobiSim on different scales of geographic maps. Our experiments show that MobiMix offers high level of anonymity and high level of resilience to timing and transition attacks, compared to existing mix-zone approaches.

Anonymizing continuous queries with delay-tolerant mix-zones over road networks

This paper presents a delay-tolerant mix-zone framework for protecting the location privacy of mobile users against continuous query correlation attacks. First, we describe and analyze the continuous query correlation attacks (CQ-attacks) that perform query correlation based inference to break the anonymity of road network-aware mix-zones. We formally study the privacy strengths of the mix-zone anonymization under the CQ-attack model and argue that spatial cloaking or temporal cloaking over road network mix-zones is ineffective and susceptible to attacks that carry out inference by combining query correlation with timing correlation (CQ-timing attack) and transition correlation (CQ-transition attack) information. Next, we introduce three types of delay-tolerant road network mix-zones (i.e., temporal, spatial and spatio-temporal) that are free from CQ-timing and CQ-transition attacks and in contrast to conventional mix-zones, perform a combination of both location mixing and identity mixing of spatially and temporally perturbed user locations to achieve stronger anonymity under the CQ-attack model. We show that by combining temporal and spatial delay-tolerant mix-zones, we can obtain the strongest anonymity for continuous queries while making acceptable tradeoff between anonymous query processing cost and temporal delay incurred in anonymous query processing. We evaluate the proposed techniques through extensive experiments conducted on realistic traces produced by GTMobiSim on different scales of geographic maps. Our experiments show that the proposed techniques offer high level of anonymity and attack resilience to continuous queries.

Preserving location privacy without exact locations in mobile services

Privacy preservation has recently received considerable attention in location-based services (LBSs). A large number of location cloaking algorithms have been proposed for protecting the location privacy of mobile users. However, most existing cloaking approaches assume that mobile users are trusted. And exact locations are required to protect location privacy, which is exactly the information mobile users want to hide. In this paper, we propose a p-anti-conspiration privacy model to anonymize over semi-honest users. Furthermore, two k*NNG-based cloaking algorithms, vk*NNCA and ek*NNCA, are proposed to protect location privacy without exact locations. The efficiency and effectiveness of the proposed algorithms are validated by a series of carefully designed experiments. The experimental results show that the price paid for location privacy protection without exact locations is small.

Protecting Location Privacy against Location-Dependent Attacks in Mobile Services

Privacy protection has recently received considerable attention in location-based services. A large number of location cloaking algorithms have been proposed for protecting the location privacy of mobile users. In this paper, we consider the scenario where different location-based query requests are continuously issued by mobile users while they are moving. We show that most of the existing k-anonymity location cloaking algorithms are concerned with snapshot user locations only and cannot effectively prevent location-dependent attacks when users' locations are continuously updated. Therefore, adopting both the location k-anonymity and cloaking granularity as privacy metrics, we propose a new incremental clique-based cloaking algorithm, called ICliqueCloak, to defend against location-dependent attacks. The main idea is to incrementally maintain maximal cliques needed for location cloaking in an undirected graph that takes into consideration the effect of continuous location updates. Thus, a qualified clique can be quickly identified and used to generate the cloaked region when a new request arrives. The efficiency and effectiveness of the proposed ICliqueCloak algorithm are validated by a series of carefully designed experiments. The experimental results also show that the price paid for defending against location-dependent attacks is small.

Fingerprinting Mobile User Positions in Sensor Networks: Attacks and Countermeasures

We demonstrate that the network flux over the sensor network provides fingerprint information about the mobile users within the field. Such information is exoteric in the physical space and easy to access through passive sniffing. We present a theoretical model to abstract the network flux according to the statuses of mobile users. We fit the theoretical model with the network flux measurements through Nonlinear Least Squares (NLS) and develop an algorithm that iteratively approaches the NLS solution by Sequential Monte Carlo Estimation. With sparse measurements of the flux information at individual sensor nodes, we show that it is easy to identify the mobile users within the network and instantly track their movements without breaking into the details of the communicational packets. Our study indicates that most of existing systems are vulnerable to such attack against the privacy of mobile users. We further propose a set of countermeasures that redistribute and reshape the network traffic to preserve the location privacy of mobile users. With a trace driven simulation, we demonstrate the substantial threats of the attacks and the effectiveness of the proposed countermeasures.

A Novel Anonymous Mutual Authentication Protocol With Provable Link-Layer Location Privacy

Location privacy of mobile users (MUs) in wireless communication networks is very important. Ensuring location privacy for an MU is an effort to prevent any other party from learning the MU's current and past locations. In this paper, we propose a novel anonymous mutual authentication protocol with provable link-layer location privacy preservation. We first formulate the security model on the link-layer, forward-secure location privacy, which is characterized by the fact that even when an attacker corrupts an MU's current location privacy, the attacker should be kept from knowing how long the MU has stayed at the current location. Then, based on the newly devised keys with location and time awareness, a novel anonymous mutual authentication protocol between the MUs and the access point (AP) is proposed. To the best of our knowledge, this is the first developed anonymous mutual authentication scheme that can achieve provable link-layer, forward-secure location privacy. To improve efficiency, a preset in idle technique is exercised in the proposed scheme, which is further compared with a number of previously reported counterparts through extensive performance analysis.

The quest for personal control over mobile location privacy

How to protect location privacy of mobile users is an important issue in ubiquitous computing. However, location privacy protection is particularly challenging: on one hand, the administration requires all legitimate users to provide identity information in order to grant them permission to use its wireless service; on the other hand, mobile users would prefer not to expose any information that could enable anyone, including the administration, to get some clue regarding their whereabouts; mobile users would like to have complete personal control of their location privacy. To address this issue, we propose an authorized-anonymous-ID-based scheme; this scheme effectively eliminates the need for a trusted server or administration, which is assumed in the previous work. Our key weapon is a cryptographic technique called blind signature, which is used to generate an authorized anonymous ID that replaces the real ID of an authorized mobile device. With authorized anonymous IDs, we design an architecture capable of achieving complete personal control over location privacy while maintaining the authentication function required by the administration.