Transport and processing of perishable information: essential infrastructure for location based services

Recent advances in mobile communication and development of sophisticated equipments lead to the wide spread use of Location Based Services (LBS). A major concern for large-scale deployment of LBSs is the potential abuse of their client location data, which may imply sensitive personal information. Protecting location information of the mobile user is challenging because a location itself may reveal user identity. Several schemes have been proposed for location cloaking. In our paper, we propose a generic Enhanced LBS Reference Model (ELRM), which describes the concept, the architecture and the functionalities for location privacy in LBS. As per the architecture, the system ensures location privacy, without trusting anybody including the peers or LBS servers. The system is fully distributed and evaluation shows its efficiency and high level of privacy with QoS.

Location-based services (LBSs) are a crucial element of the Internet of Things (IoT) and have garnered significant attention from both researchers and users, driven by the rise of wireless devices and a growing user base. However, the use of LBS-enabled applications carries several risks, as users must provide their real locations with each query. This can expose them to potential attacks from the LBS server, leading to serious issues like the theft of personal information. Consequently, protecting location privacy is a vital concern. To address this, location dummy-based methods are employed to safeguard the location privacy of LBS users. However, location dummy-based approaches also suffer from problems such as low resistance against inference attacks and the generation of strong dummy locations, an issue that is considered an open problem. Moreover, generating many location dummies to achieve a high privacy protection level leads to high network overhead and requires high computational capabilities on the mobile devices of the LBS users, and such devices are limited. In this paper, we introduce the Caching-Aware Double-Dummy Selection (CaDDSL) algorithm to protect the location privacy of LBS users against homogeneity location and semantic location inference attacks, which may be applied by the LBS server as a malicious party. Then, we enhance the CaDDSL algorithm via encapsulation with agents to solve the tradeoff between generating many dummies and large network overhead by proposing the Cache-Aware Overhead-Aware Dummy Selection (CaOaDSL) algorithm. Compared to three well-known approaches, namely GridDummy, CirDummy, and Dest-Ex, our approach showed better performance in terms of communication cost, cache hit ratio, resistance against inference attacks, and network overhead.

Recently, the ubiquitousness of smartphones and tablet computers have changed the style of people’s daily life. With this tendency, location based service (LBS) has become one of the prosperous types of service along with the wireless and positioning technology development. However, as the LBS server needs precise location information about the user to provide service result, the procedure of LBS may reveal location privacy, especially when a user is utilizing continuous query along the road. In continuous query, attributes of the user are released inadvertently with per-query, and the information can be collected by an adversary as background knowledge to correlate the location trajectory and infer the personal privacy. Although, a user can employ a central server (CS) to provide privacy preservation for his location, the trustfulness of CS still is without testified and it is usually considered as an un-trusted entity. Thus, in this paper, the trustfulness of CS is verified by a game tree, and then with the result we propose a hash based attribute anonymous scheme (short for HBAA) to obfuscate the attributes released in each query along the road. With the help of HBAA, the CS has no opportunity to get any information about the user who sends his query for generalization service. Furthermore, as the set of attributes is transmitted into a fixed length of hash value, the processing time that spent in attribute generalization is stripped down and the performance of executive efficiency is improved. At last, security analysis and simulation experiment are proposed, and then results of security proving as well as simulation experiments further reflect the superiority of our proposed scheme.

Location Based Services (LBS) are becoming very popular with today’s users. Some of these LBS require users to continuously send requests for services. This lead to leakages of both location and query contents to malicious adversaries. Further, if users are constrained by the nature of the road networks, an adversary can follow their path trajectory with ease. Most of the current privacy preserving solutions focus on temporal and spatial cloaking based methods to protect users’ location privacy. However, these solutions are vulnerable when subjected to continuous query environments. In this paper, we propose an optimized solution that preserves privacy for users’ trajectory for continuous LBS queries in road networks. First, we deploy a trusted third party architecture to provide anonymity for users as they use LBS services. Second, we utilize mix zone techniques and design two algorithms. The first algorithm, Abstraction Graph (AG), selects a sample of mix zones that satisfy the user desired privacy level under the acceptable service availability condition. The second algorithm, Optimized Decision Graph (ODG), utilizes the generated graph to find an optimal solution for the placement of mix zones through decomposition, chunking and replacement strategies. Finally, we analyze the capability of our algorithms to withstand attacks prone to mix zones and carry out experiments to verify this. The experiments results show that our Algorithms preserve privacy for users based on their privacy and service availability conditions.

The privacy leakage resulting from location-based service (LBS) has become a critical issue. To preserve user privacy, many previous studies have investigated to prevent LBS servers from user privacy theft. However, they only consider whether the peers are innocent or malicious but ignore the relationship between the peers, whereas such a relationship between each pairwise of users affects the privacy leakage tremendously. For instance, a user has less concern of privacy leakage from a social friend than a stranger. In this paper, we study cyber-physical-social (CPS) aware method to address the privacy preserving in the case that not only LBS servers but also every other participant in the network has the probability to be malicious. Furthermore, by exploring the physical coupling and social ties among users, we construct CPS-aware privacy utility maximization (CPUM) game. We then study the potential Nash equilibrium of the game and show the existence of Nash equilibrium of CPUM game. Finally, we design a CPS-aware algorithm to find the Nash equilibrium for the maximization of privacy utility. Extensive evaluation results show that the proposed approach reduces privacy leakage by 50% in the case that malicious servers and users exist in the network.

With the recent growth of mobile communication, location-based services (LBSs) are getting much attention. While LBSs provide beneficial information about points of interest (POIs) such as restaurants or cafes near users, their current location could be revealed to the server. Lien et al. have recently proposed a privacy-preserving k-nearest neighbor (k-NN) search with additive homomorphic encryption. However, it requires heavy computation due to unnecessary multiplication in the encryption domain and this causes intolerant burden on the server. In this paper, we propose a lightweight private circular query protocol (LPCQP) for privacy-preserving k-NN search with additive and multiplicative homomorphism. Our proposed scheme divides a POI-table into some sub-tables and aggregates them with homomorphic cryptography in order to remove unnecessary POI information for the request user, and thus the computational cost required on the server is reduced. We evaluate the performance of our proposed scheme and show that our scheme reduces the computational cost on the LBS server while keeping high security and high accuracy.

Location Based Service (LBS) 'as a kind of emerging applications based on the mobile Internet technology, is one of the most potential mobile value-added services. With the mobile network positioning technology and electronic map platform, it provides position information of the traffic, tourist attractions, shopping center etc. to users. This paper studies on the background, significance, types and problems of LBS. Basing on the analysis of the present situation and development trend of LBS in China and abroad, this paper summarizes the LBS service development experience, and puts forward the service model of LBS for its development.

With the rise of mobile technology in the recent years, a brand new form of service, Location Based Service (LBS), has emerged. Well performed and reliable LBS applications or services heavily rely on the accuracy and integrity of location data in addition to the timely update attributes. However, manually identifying and organizing the location information can be very time consuming and human error is another inevitable fact. Not many previous studies have been done concerning how to improve the accuracy and quality of location data through the data de-duplication process using web mining techniques. In this paper, we propose an efficient and cost-effective way to integrate the location information collected from multiple data sources and automate the de-duplication process with the help of data mining techniques. A series of experiments have been conducted to prove that the proposed approach can effectively and efficiently handle the location data integration tasks.

Location-Based Services (LBSs) are one of the most frequently used mobile applications in the modern society. Geo-Indistinguishability (Geo-Ind) is a promising privacy protection model for LBSs since it can provide formal security guarantees for location privacy. However, Geo-Ind undermines the statistical location distribution of users on the LBS server because of perturbed locations, thereby disabling the server to provide distribution-based services (e.g., traffic congestion maps). To overcome this issue, we give a privacy definition, called DistPreserv, to enable the LBS server to acquire valid location distributions while providing users with strict location protection. Then we propose a privacy-preserving LBS scheme to benefit both users and the server, in which a location perturbation mechanism is designed to achieve the given definition under the guide of the incentive compatibility, and a retrieval area determination method is presented to ensure query accuracy of users by using the dynamic programming on the two-dimensional map plane. Finally, we theoretically prove that the designed mechanism can achieve the definition of DistPreserv and the property of incentive compatibility. Experimental explorations using a real-world dataset indicate that our proposal prominently improves the availability of users location distributions by over 90%, while providing high precision and recall of queries.

The ubiquity of mobile devices with global positioning functionality (e.g., GPS and Assisted GPS) and Internet connectivity (e.g., 3G and Wi-Fi) has resulted in widespread development of location-based services (LBS). Typical examples of LBS include local business search, e-marketing, social networking, and automotive traffic monitoring. Although LBS provide valuable services for mobile users, revealing their private locations to potentially untrusted LBS service providers pose privacy concerns. In general, there are two types of LBS, namely, snapshot and continuous LBS. For snapshot LBS, a mobile user only needs to report its current location to a service provider once to get its desired information. On the other hand, a mobile user has to report its location to a service provider in a periodic or on-demand manner to obtain its desired continuous LBS. Protecting user location privacy for continuous LBS is more challenging than snapshot LBS because adversaries may use the spatial and temporal correlations in the user's a sequence of location samples to infer the user's location information with a higher degree of certainty. Such user spatial trajectories are also very important for many applications, e.g., business analysis, city planning, and intelligent transportation. However, publishing original spatial trajectories to the public or a third party for data analysis could pose serious privacy concerns. Privacy protection in continuous LBS and trajectory data publication has increasingly drawn attention from the research community and industry. In this chapter, we describe the state-of-the-art privacy-preserving techniques for continuous LBS and trajectory publication.

The combination of wireless mobile service and spatial information service introduces location-based services (LBS). LBS utilizes GIS, spatial positioning and embedded technologies in mobile computing environment to provide mobile terminal users location relevant information. The purpose of this paper is to design a framework supporting advanced LBS over 4G mobile-IP networks, with the transition of existing LBS services to an all mobile-IP based architecture. The architecture, compositions and challenges for LBS in 4G are discussed. The detailed functions of each component are presented. On the base of this, the research points in implementation including positioning methods, new service management model, mobile spatial data compression and transmission, data cache in mobile terminals are also proposed.

With the development of mobile communication and other related technology, LBS (Location Based Service) is becoming an indispensable part of people's daily life. Studying on services composition modeling in LBS is necessary, but current services composition modeling does not take into account the location uncertainty, real-time service and other factor related to location context. In this paper, we present a TCPN (Timing Constraint Petri Nets) based approach to model the dynamic LBS composition and argue some functional validations of them, including the discussion of schedulability and reachability of the model and the transition. Finally, we used the CPN Tools to simulate the model, so the whole generation process of LBS services composition will be shown much more intuitive and a better understanding of the model achieved by controlling the firing sequence of transition in the model.

Location Based Service (LBS) refers to class of applications that provides services based on the current or a known location. The location information can be obtained through the mobile communication network or the Global Navigation Satellite Systems (GNSS) (8). But in all the cases after extracting information we have to make an exhaustive processing over this extracted geographic information. And this exhaustive processing requires a significant amount of time if it is performed by traditional techniques. Artificial Intelligence, on the other hand, has been proven to be useful in the interpretation of spatial resource information. On of the artificial intelligence technique to support spatial processing is artificial neural networks (ANNs). The recent developments in ANN technology have made it more ofan applied mathematical technique that has some similarities to the human brain. The integration of LBS with AIoffers a potential mechanismto lower the analysis-time of spatial information. One major advantage is that this integration allows the interpretive result from a small area to be transferred to a larger, geographically similar area. The objective of this paper is to discuss about the relativity of LBS services and Artificial Intelligence technologies and to uncover the benefits of converging LBS and AI. Index Terms—LBS, GIS, AI, ANNs.

무선 통신 기술 및 GPS(Global Positioning System)등의 발달로 인하여 위치 기반 서비스 (Location-Based Services: LBS)가 크게 발전하는 추세이다. 그러나 위치 기반 서비스를 이용하기 위해 질의 요청자는 자신의 정확한 위치 정보를 위치 기반 서비스 제공자에게 전송해야 한다. 따라서 위치 기반 서비스를 제공하면서 질의 요청자의 위치 정보를 보호하는 것이 중요한 과제이다. 이 문제를 해결하기 위해, 기존 기법은 실제 사용자의 위치를 숨기며 네트워크 사용을 줄일 수 있는 2PASS 기법을 사용하였다. 그러나 이 기법은 실제 사용자 분포를 고려하지 않기 때문에 실제 사용자 위치 보호를 완전히 보장하지 않는다. 따라서 본 논문에서는 K-anonymity를 보장하는 가중치 근접성 그래프 기반 최근접 질의처리 알고리즘을 제안한다. 제안하는 알고리즘은 질의 영역 내 K-anonymity를 보장함으로써 사용자의 위치 정보를 보호할 뿐만 아니라 불필요한 질의 결과 탐색을 줄여 네트워크 효율을 증가시킨다. 마지막으로, 성능평가를 통해 제안하는 기법이 기존 연구에 비해 질의 처리 시간 및 네트워크 효율 측면에서 우수함을 보인다. Location-based services (LBS) are increasingly popular due to the improvement of geo-positioning capabilities and wireless communication technology. However, in order to enjoy LBS services, a user requesting a query must send his/her exact location to the LBS provider. Therefore, it is a key challenge to preserve user's privacy while providing LBS. To solve this problem, the existing method employs a 2PASS cloaking framework that not only hides the actual user location but also reduces bandwidth consumption. However, 2PASS does not fully guarantee the actual user privacy because it does not take the real user distribution into account. Hence, in this paper, we propose a nearest neighbor query processing algorithm that supports K-anonymity property based on the weighted adjacency graph(WAG). Our algorithm not only preserves the location of a user by guaranteeing k-anonymity in a query region, but also improves a bandwidth usage by reducing unnecessary search for a query result. We demonstrate from experimental results that our algorithm outperforms the existing one in terms of query processing time and bandwidth usage.

Public Transport Information Service which use smartphone Apps has received attention as the way of solution that reduced transport problem. Smartphone can offer real-time information because of a LBS(Location Based Service) system. This study try to find out which factor affect mode choice ratio of public transport, especially smartphone Apps. The result shows that rising oil price, traffic congestion, public information service with smartphone apps, BIS(Bus Information System) factors get 0.39, 0.27, 0.18, 0.16 scores with paired comparison. Younger and student respondents prefer smart phone public information service. Decision Tree shows that the most important decision factor is smartphone information service factor.