Pseudonym changing strategy with multiple mix zones for trajectory privacy protection in road networks

In road network, vehicles' location may be identified, and their transmissions may even tracked by eavesdrops (eg, safety messages) that contain sensitive personal information such as identity and location of the vehicle. This type of communication leads to breaking the users' trajectory privacy. Frequently changing pseudonyms are widely accepted as a solution that protects the trajectory privacy of users in road networks. However, this solution may become invalid if a vehicle changes its pseudonym at an improper occasion. To cope with this issue, we presented an efficient pseudonym change strategy with multiple mix zones scheme to provide trajectory privacy for road network. In addition, we protected vehicles against linkability attack by cheating mechanism. Henceforth, we constructed a cheating detection mechanism which allows the vehicles to verify whether the pseudonym change process is successful or not and also detect to malicious vehicles. In this way, users' trajectory privacy can be improved. Finally, by taking the anonymity set size (ASS) as the trajectory privacy metric, we exhibit by means of simulations that the proposed scheme is effective in multiple networks scenarios.     

Search me if you can: Multiple mix zones with location privacy protection for mapping services

The mobile vehicle is gaining popularity nowadays using map services like Google Maps and other mapping services. However, map services users have to expose sensitive information like geographic locations (GPS coordinates) or address to personal privacy concerns as users share their locations and queries to obtain desired services. Existing mix zones location privacy protection methods are most general purposed and theoretical value while not applicable when applied to provide location privacy for map service users. In this paper, we present new (multiple mix zones location privacy protection) MMLPP method specially designed for map services on mobile vehicles over the road network. This method enables mobile vehicle users to query a route between 2 endpoints on the map, without revealing any confidential location and queries information. The basic idea is to strategically endpoints to nearby ones, such that (1) the semantic meanings encoded in these endpoints (eg, their GPS coordinates) change much, ie, location privacy is protected; (2) the routes returned by map services little change, ie, services usability are maintained. Specifically, a mobile client first privately retrieves point of interest close to the original endpoints, and then selects 2 points of interest as the shifted endpoints satisfying the property of geoindistinguishability. We evaluate our MMLPP approach road network application for GTMobiSim on different scales of map services and conduct experiments with real traces. Results show that MMLPP strikes a good balance between location privacy and service usability.     

Traffic Volume Prediction Based on Multi-Sources GPS Trajectory Data by Temporal Convolutional Network

Predicting urban traffic volume is of great significance to traffic management and urban construction. An accurate prediction model can help drivers optimize driving routes, allocate resources reasonably and reduce urban traffic congestion. Most of the existing studies do not consider the complex nonlinear spatio-temporal relationship. In the spatial dimension, they do not consider the impact of regional semantics and regional interactions. In the temporal dimension, they ignore the impact of long-term historical information and key time points. Aiming at the complexity of traffic data, in this paper, we design a ResNet-TCN model to predict the urban traffic volume. Firstly, we construct and extract features from the vehicle GPS tracking and external information, such as velocity, time, location and weather. Then, we obtain regional semantic information by the ResNet model and combine the weights of the regional division with the average vehicle velocity into a two-channel matrix. We extract the key features of the matrix sequence and predict the velocity by the TCN model. Finally, we estimate the traffic volume through a traffic volume inference model in the traffic field. We conduct a large number of experiments on the actual dataset of Chengdu and compare our model with the existing models. The experimental results show that our method has better performance on prediction accuracy.

     

LTPPM: a location and trajectory privacy protection mechanism in participatory sensing

The ubiquity of mobile devices has facilitated the prevalence of participatory sensing, whereby ordinary citizens use their private mobile devices to collect regional information and to share with participators. However, such applications may endanger the users' privacy by revealing their locations and trajectories information. Most of existing solutions, which hide a user's location information with a coarse region, are under k‐anonymity model. Yet, they may not be applicable in some participatory sensing applications that require precise location information. The goals are seemingly contradictory: to protect a user's location privacy while simultaneously providing precise location information for a high quality of service. In this paper, we propose a method to meet both goals. Through selecting a certain number of a user's partners, it can protect the user's location privacy while providing precise location information. The user's trajectory privacy can be protected by constructing several trajectories that are similar to the user's trajectory in an interval time T. Finally, we utilize a new metric, called slope ratio, to evaluate the partners' selection algorithm that we proposed. Then, we measure the privacy level that the location and trajectory privacy protection mechanism (LTPPM) can achieve. The analysis and simulation results show that LTPPM can protect the user's location and trajectory privacy effectively and also provide a high quality of service in participatory sensing. Copyright © 2012 John Wiley & Sons, Ltd.     

Energy Efficient Multi-hop Cooperative Transmission Protocol for Large Scale Mobile Ad hoc Networks

A great advancement has been made in intelligent transportation system and communication technologies in order to exchange secure information between automobiles, facilities provider have led an frame over road network. The intelligent transportation system provides an efficient traffic system for drivers, so that there must be less risk to users. In order to design a secure communication protocol among V & V and V & I is a challenging problem. In order to reduce the chance of attacks and increase privacy level, crypto graphic tools provides the feasible solution. In this paper, we proposed pseudonym changing strategy with mix zones (1) Anonymous authentication: the message should be authenticated by issuer due to mix zone and cryptographic tools secure message without any attack. (2) Privacy: Communication contents are confidential due to encrypted messages during communications also improving the scalability through address configuration scheme to reduce computational cost. (3) Efficiency: low storage requirements, The velocity and distance factors may also consider secure measurement, message delivery, overhead and coverage,packet delivery rate, reduce latency and overhead not only by computation cost and time but also compared our scheme, fast delivery rate, low latency and maximum coverage in order to enhance privacy against malicious attacks.

     

Dynamic Mix-zone scheme with joint-entropy based metric for privacy-perserving in IoV

Aiming at the weak flexibility and lack of users’ transparency existing in the current Mix-zone schemes for Internet of vehicle (IoV),a dynamic was proposed for Mix-zone construction with traffic adaption,which could construct a Mix-zone for the vehicles dynamically according to the traffic conditions for changing pseudonym at anytime and anywhere.This kind of Mix-zone could achieve privacy-preserving based on the identity and location.In addition,a novel traffic-adaptive metric was presented for classifying the privacy leveled in Mix-zone,which applied the normalization quantitation to measure the degree of Mix-zone’s privacy demanding by the current region.It was verified that the joint entropy-based privacy measuring model and the Mix-zone construction scheme by utilizing the trajectory data of taxis in certain district in Shenzhen city.The experimental shows that the proposed combination entropy-based model could depict the proportional relationship between the traffic scene parameters and the privacy-preserving degrees.The scheme is better in performance over the related methods,and strikes a good balance between location privacy and service usability.     

Infrastructure‐based vehicular congestion detection scheme for V2I

With the increasing number of vehicles, traffic jam becomes one of the major problems of the fast‐growing world. Intelligent transportation system (ITS) communicates perilous warnings and information on forthcoming traffic jams to all vehicles within its coverage region. Real‐time traffic information is the prerequisite for ITS applications development. In this paper, on the basis of the vehicle‐to‐infrastructure (V2I) communication, a novel infrastructure‐based vehicular congestion detection (IVCD) scheme is proposed to support vehicular congestion detection and speed estimation. The proposed IVCD derives the safety time (time headway) between vehicles by using iterative content‐oriented communication (COC) contents. Meanwhile, the roadside sensor (RSS) provides an infrastructure framework to integrate macroscopic traffic properties into the estimation of both the traffic congestion and vehicle safety speed. The main responsibilities of RSS in IVCD are to preserve privacy, aggregate data, store information, broadcast routing table, estimate safety speed, detect traffic jam, and generate session ID (S‐ID) for vehicles. Monte Carlo simulations in four typical Chinese highway settings are presented to show the advantage of the proposed IVCD scheme over the existing Greenshield's and Greenberg's macroscopic congestion detection schemes in terms of the realized congestion detection performance. Real road traces generated by Simulation of Urban Mobility (SUMO) over NS‐3.29 are utilized to demonstrate that the proposed IVCD scheme is capable of effectively controlling congestion in both single and multilane roads in terms of density and speed health with previous schemes in this field.     

基于差分隐私模型的位置轨迹发布技术研究

位置轨迹大数据的安全分享、发布需求离不开位置轨迹隐私保护技术支持。在差分隐私出现之前,K-匿名及其衍生模型为位置轨迹隐私保护提供了一种量化评估的手段,但其安全性严重依赖于攻击者所掌握的背景知识,当有新的攻击出现时模型无法提供完善的隐私保护。差分隐私技术的出现有效地弥补了上述问题,越来越多地应用于轨迹数据隐私发布领域中。该文对基于差分隐私理论的轨迹隐私保护技术进行了研究与分析,重点介绍了差分隐私模型下位置直方图、轨迹直方图等空间统计数据发布方法,差分隐私模型下轨迹数据集发布方法,以及连续轨迹实时发布隐私保护模型。与此同时,在对现有方法对比分析的基础上,提出了未来的重点发展方向。     

基于视频车辆运动轨迹场的交通事件检测方法*

针对现有交通事件检测算法存在的模型复杂、运算量大,并且检测结果滞后的缺点,提出了一种基于视频车辆运动轨迹场的交通事件检测方法.该方法以基于视频的车辆跟踪轨迹为基础,形成正常车辆运动轨迹场,进而将当前车辆轨迹矢量与正常轨迹场矢量比较,最终实现了对超速、慢行、变道和逆行的异常交通事件的检测.研究结果表明,与传统方法相比,该方法算法简单,并且可以快速、准确地对异常交通事件进行检测.     

Location prediction algorithm for a nonlinear vehicular movement in VANET using extended Kalman filter

Vehicular ad-hoc network (VANET) is an essential component of the intelligent transportation system, that facilitates the road transportation by giving a prior alert on traffic condition, collision detection warning, automatic parking and cruise control using vehicle to vehicle (V2V) and vehicle to roadside unit (V2R) communication. The accuracy of location prediction of the vehicle is a prime concern in VANET which enhances the application performance such as automatic parking, cooperative driving, routing etc. to give some examples. Generally, in a developed country, vehicle speed varies between 0 and 60 km/h in a city due to traffic rules, driving skills and traffic density. Likewise, the movement of the vehicle with steady speed is highly impractical. Subsequently, the relationship between time and speed to reach the destination is nonlinear. With reference to the previous work on location prediction in VANET, nonlinear movement of the vehicle was not considered. Thus, a location prediction algorithm should be designed by considering nonlinear movement. This paper proposes a location prediction algorithm for a nonlinear vehicular movement using extended Kalman filter (EKF). EKF is more appropriate contrasted with the Kalman filter (KF), as it is designed to work with the nonlinear system. The proposed prediction algorithm performance is measured with the real and model based mobility traces for the city and highway scenarios. Also, EKF based prediction performance is compared with KF based prediction on average Euclidean distance error (AEDE), distance error (DE), root mean square error (RMSE) and velocity error (VE).     

Efficient User Based Authentication Protocol for Location Based Services Discovery Over Road Networks

In road network, the decision accuracy of event message under location privacy-enhanced scheme, fast forwarding falsification message, and collusion attacks are all knotty problems that arise in event trust management. In this paper, we proposed protocol verification to check vehicles activity in privacy manner. We proposed a new method to adjusted vehicles speed which reduces the vehicle delay suffers from network gap problem. We established privacy preserving authentication protocol to verify vehicle activities in the term of privacy preserving manner. Vehicles moving trend, velocity differences, and distance differences are taken into consideration so, to maintain as many common users as possible to reduce the cost. The cost analysis and performance evaluation indicate that our frame-work can reduced cost factor and achieve good performance. The proposed model achieved reliably and efficiently with packet rate information. The evaluation experiments based on NS-3 to our improve user’s authenticated key establishment protocol has comparatively shorter time response, reduce cost, less packet lost information and enhanced privacy preservation compared with existing methods.     

Privacy‐preserving authentication schemes for vehicular ad hoc networks: a survey

Vehicular ad hoc networks (VANETs) are expected in improving road safety and traffic conditions, in which security is essential. In VANETs, the authentication of the vehicular access control is a crucial security service for both inter‐vehicle and vehicle–roadside unit communications. Meanwhile, vehicles also have to be prevented from the misuse of the private information and the attacks on their privacy. There is a number of research work focusing on providing the anonymous authentication with preserved privacy in VANETs. In this paper, we specifically provide a survey on the privacy‐preserving authentication (PPA) schemes proposed for VANETs. We investigate and categorize the existing PPA schemes by their key cryptographies for authentication and the mechanisms for privacy preservation. We also provide a comparative study/summary of the advantages and disadvantages of the existing PPA schemes. Lastly, the open issues and future objectives are identified for PPA in VANETs. Copyright © 2014 John Wiley & Sons, Ltd.     

Vehicle trajectory prediction algorithm in vehicular network

Vehicular ad hoc network has become an important component of the intelligent transportation system, what’s more, the vehicle trajectory prediction has gradually become one of the hotter issues in this research. Vehicle trajectory prediction cannot only provide accurate location services, but also can monitor traffic conditions in advance, and then recommend the best route for the vehicle. For this purpose, this research established a new method for vehicle trajectory prediction (TPVN), which is mainly applied to predict the vehicle trajectory in the short term. Based on the regularity of vehicle movement, the algorithm is helpful to predict the vehicle trajectory so as to estimate the position of the vehicle motion probability. To improve the prediction accuracy, the motion patterns are divided into two types: simple pattern and complex pattern. The advantage of the TPVN algorithm is that the calculation result not only predicts the movement behavior of vehicles in different motion patterns but also the probability distribution of all possible trajectories of the vehicle in the future. Simulation on a large number of true trajectory datasets shows that the performance of TPVN outperforms than other classical algorithms.

     

Distance and clustering‐based energy‐efficient pseudonyms changing strategy over road network

To improve the fairness, the energy consumption changing pseudonyms needs to be taken into account. Existing works focus on changing velocity‐based pseudonyms changing strategy and short changes interval with limited coverage, but due to similar velocity and short changes, internal attacker guesses easily known communication and location information due to location information of vehicle on tracking, which may expose adversary private information, and frequently, pseudonyms changing occurs due to movement of vehicles' similar velocity and short coverage, which may cause serious attack of vehicle. To overcome this problem, distance and cluster can be performed. In this work, we proposed distance and cluster‐based energy pseudonyms changing method for road network. We proposed distance and energy‐based clustering routing service over road network, the cluster head elected to depend on random number of distance and energy to change pseudonyms of vehicles. An each interval to be establish cluster head vehicle deployed while selects the operation mode and informs the cluster members of the selected mode through beacon signal. The cluster head vehicle node performs the pseudonyms changing based on the predicted distance and energy of the cluster member to use clustering optimization. The data of whole network send to report server through these nodes while near the RSU, and the vehicles in this area will use less energy to change the pseudonyms. The simulation results show that the proposed method enhances pseudonyms changing strategy less consumption and delays sufficient privacy level each vehicle also our method has outperform compare with existing methods than we use Sumo simulation and Matlab tools to verify our proposed method. Our proposed method outperformed in terms of pseudonym changing energy efficiency to careful attention during the cluster formation process, stable and balanced clusters that prolong the network lifetime, increases distances to more CH vehicles connectivity to makes clustering group and changing their pseudonyms in terms of high level privacy and finally, CH nodes use Dijkstra's algorithm use MST among the vehicles nodes depend on existing road networks to follow shortest path selection roads in terms of high connectivity probability of CH and stable structure of the network decreases the topology changes and thus,the clustering overhead is reduced.     

基于位置语义的路网位置隐私保护

移动用户在享受基于位置的服务（LBS）的同时受到位置隐私泄露的威胁,因而提供有效的位置隐私保护策略至关重要。传统的位置隐私保护方法主要采用空间匿名的方式,若攻击者获得了更多与匿名空间相关的背景知识,尤其是与位置相关的语义信息,就会严重降低匿名效果。为了防止由位置语义分析造成的敏感位置信息泄露,并根据移动用户活动范围大多限定为道路网络的特点,提出一种基于位置语义的路网位置隐私保护方法,充分考虑了用户的个性化隐私需求,并通过实验验证了方法的可行性及有效性。     

Delay‐aware privacy‐preserving location‐based services under spatiotemporal constraints

The ubiquitous use of location‐based services (LBS) through smart devices produces massive amounts of location data. An attacker, with an access to such data, can reveal sensitive information about users. In this paper, we study location inference attacks based on the probability distribution of historical location data, travel time information between locations using knowledge of a map, and short and long‐term observation of privacy‐preserving queries. We show that existing privacy‐preserving approaches are vulnerable to such attacks. In this context, we propose a novel location privacy‐preserving approach, called KLAP, based on the three fundamental obfuscation requirements: minimum k ‐locations, l ‐diversity, and privacy a rea p reservation. KLAP adopts a personalized privacy preference for sporadic, frequent, and continuous LBS use cases. Specifically, it generates a secure concealing region (CR) to obfuscate the user's location and directs that CR to the service provider. The main contribution of this work is twofold. First, a CR pruning technique is devised to establish a balance between privacy and delay in LBS usage. Second, a new attack model called a long‐term obfuscated location tracking attack, and its countermeasure is proposed and evaluated both theoretically and empirically. We assess KLAP with two real‐world datasets. Experimental results show that it can achieve better privacy, reduced delay, and lower communication costs than existing state‐of‐the‐art methods.     

Intelligent Privacy Protection of End User in Long Distance Education

Long distance education is an important part during the COVID-19 age. An intelligent privacy protection with higher effect for the end users is an urgent problem in long distance education. In view of the risk of privacy disclosure of location, social network and trajectory of end users in the education system, this paper deletes the location information in the location set to protect the privacy of end user by providing the anonymous set to location. Firstly, this paper divides the privacy level of social networks by weighted sensitivity, and collects the anonymous set in social networks according to the level; Secondly, after the best anonymous set is generated by taking the data utility loss function as the standard, it was split to get an anonymous graph to hide the social network information; Finally, the trajectory anonymous set is constructed to hide the user trajectory with the l-difference privacy protection algorithm. Experiments show that the algorithm presented in this paper is superior to other algorithms no matter how many anonymous numbers there are, and the gap between relative anonymity levels is as large as 5.1 and 6.7. In addition, when the privacy protection intensity is 8, the trajectory loss rate presented in this paper tends to be stable, ranging from 0.005 to 0.007, all of which are less than 0.01. Meanwhile, its clustering effect is good. Therefore, the proportion of insecure anonymous sets in the algorithm in this paper is small, the trajectory privacy protection effect is good, and the location, social network and trajectory privacy of distance education end users are effectively protected.

     

Autonomous parking control for intelligent vehicles based on a novel algorithm

Along with the increasing number of vehicles, parking space becomes narrow gradually, safety parking puts forward higher requirements on the driver's driving technology. How to safely, quickly and accurately park the vehiclo to parking space right? This paper presents an automatic parking scheme based on trajectory planning, which analyzing the mechanical model of the vehicle, establishing vehicle steering model and parking model, coming to the conclusion that it is the turning radius is independent of the vehicle speed at low speed. The Matlab simulation environment verifies the correctness and effectiveness of the proposed algorithm for parking. A class of the automatic parking problem of intelligent vehicles is solved.     

Trajectory privacy protection method based on location obfuscation

In the process of continuous queries,a method of trajectory privacy protection based on location obfuscation was proposed to solve the problem that K-anonymity was difficult to guarantee user privacy in third party architectrue.Firstly,the (G-1) query obfuscation locations through the location prediction was obtained and the dummy location selection mechanism,and then sent them together with the user’s real query location to different anonymizers to form cloaking regions and sent them to the LBS server for queries,and the query results were returned to the user by different anonymizers.In this method,the user’s real query location was confused by the location obfuscation,and the attacker couldn’t deduce the user’s trajectory from a single anonymizer or the LBS server.The method can enhance the privacy of the user’s trajectory and can effectively solve the performance bottleneck in the single anonymizer structure.Security analysis shows the security of the proposed approach,and experiments show this method can reduce the number of interactions between the user and the LBS server and the overhead of the single anonymizer.     

基于稳定状态差异的停车及抛落物检测算法

近年来,道路交通状况的自动检测逐渐成为研究的热点。因为违法停车、抛落物不仅影响交通秩序、诱发交通事故而且有损城市形象,所以停车、抛落物的检测已经构成道路交通状况自动检测的重要组成部分。提出了一种自动检测停车事件及抛落物事件的方法,利用双向跟踪实现停车及抛落物的检测,通过分析目标面积实现两者的区分。实验表明,该方法实时性好、准确性强,具有很大的实用价值。