On authenticated skyline query processing over road networks

This paper introduce a geographic information is present on the internet which is in the scattered format. When user search for any place then various locations based service providers find out the results. The commonly used location based service providers are Google, Bing, Yahoo etc. we considers a novel distributed system for collaborative location-based information generation and sharing which become increasingly popular due to the explosive growth of Internet-capable and location-aware mobile devices. The results that are given by location based service providers not fully processed. The system consists of a data collector, data contributors, location-based service providers (LBSPs), and system users. The data collector gathers reviews about points-of-interest (POIs) from data contributors, while LBSPs purchase POI data sets from the data collector and allow users to perform spatial zonal top-k queries which ask for the POIs in a certain region and with the highest k ratings for an interested POI attribute. The data contributor is the peoples or users who provide the information about the place. The user can gives comment and reviews to the product or the place. It's not necessary the LBSP provides proper information. In practice, LBSPs are untrusted and may return fake query results for various bad motives, e.g., in favour of POIs willing to pay. In our approach when the data contributor searches for any query, it will process by the LBSP. But before result showing to the user, it will process by the data collector. There are various results for the same query. So each query processed and special top result according to the location will provide to the users. This paper presents three novel schemes for users to detect fake spatial snapshot and arranging top-k query results as an effort to foster the practical deployment and use of the proposed system.

Integrating heterogeneous spatial data is a crucial problem for geographical information systems (GIS) applications. Previous studies mainly focus on the matching of heterogeneous road networks or heterogeneous polygonal data sets. Few literatures attempt to approach the problem of integrating the point of interest (POI) from volunteered geographic information (VGI) and professional road networks from official mapping agencies. Hence, the article proposes an approach for integrating VGI POIs and professional road networks. The proposed method first generates a POI connectivity graph by mining the linear cluster patterns from POIs. Secondly, the matching nodes between the POI connectivity graph and the associated road network are fulfilled by probabilistic relaxation and refined by a vector median filtering (VMF). Finally, POIs are aligned to the road network by an affine transformation according to the matching nodes. Experiments demonstrate that the proposed method integrates both the POIs from VGI and the POIs from official mapping agencies with the associated road networks effectively and validly, providing a promising solution for enriching professional road networks by integrating VGI POIs.

This paper considers a novel distributed system for collaborative location-based information generation and sharing which become increasingly popular due to the explosive growth of Internet-capable and location-aware mobile devices. The system consists of a data collector, data contributors, location-based service providers (LBSPs), and system users. The data collector gathers reviews about points-of-interest (POIs) from data contributors, while LBSPs purchase POI data sets from the data collector and allow users to perform spatial top-k queries which ask for the POIs in a certain region and with the highest k ratings for an interested POI attribute. In practice, LBSPs are untrusted and may return fake query results for various bad motives, e.g., in favor of POIs willing to pay. This paper presents three novel schemes for users to detect fake spatial snapshot and moving top-k query results as an effort to foster the practical deployment and use of the proposed system. The efficacy and efficiency of our schemes are thoroughly analyzed and evaluated.

In order to achieve user's privacy preservation and data security of LBS server in road networks, a query scheme for points of interest(PoI) is proposed in this paper based on oblivious transfer and private information retrieval. The scheme can proceed in two phases: Firstly, we take vertexes in road networks as generating elements to construct a new data structure which is employed to store the distribution information and details of PoIs which are encrypted with different symmetric keys for sake of data security of LBS server. And an oblivious transfer protocol is presented based on the data structure, which facilitates a user to privately obtain the unique key materials corresponding to his interested PoIs without revealing any other PoI information in LBS server since the data is its asset. Then, a private information retrieval protocol is presented to obtain the interested PoI details, the user does not need to provide his location or explicit query content in the query, whereas he can derive an accurate result with the previous key materials, so it guarantees the user's privacy as well. Finally, performance analysis and experiments show that our scheme is more efficient and accurate in a real scenario compared with existing solutions.

Disclosure of sensitive data leads to identity theft and violation of privacy. The untrusted cloud service provider (CSP) may try to disclose/misuse the data. It is necessary to provide access control and security over the outsourced and shared data to hide it from the CSP and unauthorized users. Traditional access control schemes are prone to security threats in the cloud environment. Attribute-based access control schemes (ABAC) are well suited for the cloud environment. Attribute-based encryption (ABE) is a promising cryptographic solution to provide fine-grained access control over the shared data. It selectively shares the data among the users and hides data from the CSP and unauthorized users. It preserves the privacy of users and the security of data being shared. Users can decrypt the data only if their attributes are satisfied with the access policy associated in the ciphertext. This paper presents a comprehensive survey of the ABE schemes. Taxonomy, performance comparison, and applications of ABE schemes are dealt with. The taxonomy and performance comparison help the selection of the most suitable ABE scheme based on specific usage scenarios. Thus, the survey opens up very interesting avenues for further research in this area, which are also discussed.

With the ever-growing popularity of smartphone devices in recent years, skyline queries over spatial Web objects in road networks have received increasing attention. In the literature, various techniques have been developed to tackle skyline queries that take both spatial and non-spatial attributes into consideration. However, the existing solutions only focus on solving point-based queries, where the query location is a spatial point. We observe that in many real-life applications, the user location is often represented by a spatial range. Thus, in this paper, we study a new problem of range-based skyline queries (CRSQs) in road networks. Two efficient algorithms named landmark-based (LBA) and index-based (IBA) algorithms are proposed. We also present incremental versions of LBA and IBA to handle continuous range-based skyline queries over moving objects. Extensive experiments using real road network datasets demonstrate the effectiveness and efficiency of our proposed algorithms.

Skyline queries are well suited when retrieving data according to multiple criteria. While most previous work has assumed a centralized setting this paper considers skyline querying in a mobile and distributed setting, where each mobile device is capable of holding only a portion of the whole dataset; where devices communicate through mobile ad hoc networks; and where a query issued by a mobile user is interested only in the user's local area, although a query generally involves data stored on many mobile devices due to the storage limitations. We present techniques that aim to reduce the costs of communication among mobile devices and reduce the execution time on each single mobile device. For the former, skyline query requests are forwarded among mobile devices in a deliberate way, such that the amount of data to be transferred is reduced. For the latter, specific optimization measures are proposed for resource-constrained mobile devices. We conduct extensive experiments to show that our proposal performs efficiently in real mobile devices and simulated wireless ad hoc networks.

With the ever-increasing use of smartphones and tablet devices, location-based services (LBSs) have experienced explosive growth in the past few years. To scale up services, there has been a rising trend of outsourcing data management to Cloud service providers, which provide query services to clients on behalf of data owners. However, in this data-outsourcing model, the service provider can be untrustworthy or compromised, thereby returning incorrect or incomplete query results to clients, intentionally or not. Therefore, empowering clients to authenticate query results is imperative for outsourced databases. In this paper, we study the authentication problem for location-based arbitrary-subspace skyline queries (LASQs), which represent an important class of LBS applications. We propose a basic Merkle Skyline R-tree method and a novel Partial S4-tree method to authenticate one-shot LASQs. For the authentication of continuous LASQs, we develop a prefetching-based approach that enables clients to compute new LASQ results locally during movement, without frequently contacting the server for query re-evaluation. Experimental results demonstrate the efficiency of our proposed methods and algorithms under various system settings.

Location-based services (LBS) enable users to question the purpose of interest like restaurants, hospitals with various features. Although optimal route calculation in road network supported distance may be a combinatorial optimization problem, with the advancement of varied web mapping services, it is possible to produce travel distance for LBS applications. Road network and site data easily are often represented as a graph during a spatial database. By using different spatial functions like pg-routing Dijikstra or pg-Routing A*, we will efficiently calculate the route from it. But in case of extremely large dataset, searching route and finding nearest neighbors are far more difficult. So here a completely unique indexing method for the road network is proposed. G-Tree indexing is an indexing method that efficiently supports mainly two queries,i.e., k-NN queries, single-pair shortest path query. G-Tree indexing method is inspired by R-Tree. Inspired by R-Tree, G-Tree may be a height-balanced and scalable index, namely G-Tree, to efficiently support these queries. The space complexity of G-Tree is O(│V│log│V│), where │V│ is the number of vertices within the road network. Finding only one shortest path to a point of interest (POI) is not sufficient for many situations like road conditions, traffic, etc. So this paper also suggests the method for calculating k alternating paths to a point of interest (POI) by using Yen’s algorithm and G-Tree.

Hierarchical attribute-based encryption and scalable user revocation for sharing data in cloud servers

Skyline queries are useful in decision making applications. Skyline queries in highly mobile distributed environments have attracted many attentions recently due to the development of mobile internet device. The properties of distributed computing make skyline queries more complicated especially in any subspace. Conventional skyline algorithms do not support subspace skyline queries in distributed environment. In this paper, we focus on how to perform distributed skyline queries in any subspace according to user preference. So we propose a system model in a mobile and distributed environment. An efficient parallel algorithm for processing the Subspace Skyline Query (SSQ) using MapReduce is applied to the system model. This algorithm can report skyline points in any subspace. Meanwhile, a pruning strategy is also proposed in order to reduce the network communication and minimize the response time. We conduct experiments on real and synthetic data. Experimental results indicate that our SSQ algorithm is much more efficient. Furthermore, the pruning strategy can further improve the performance of the algorithm.

A hybrid scheme for multicast authentication over lossy networks

Mobile Cloud Computing (MCC) is getting growing interest due to its wide applicability in variety of social, industrial, and commercial mobile applications. Mobile and smart devices can share complex computational operations with Cloud Service Providers (CSPs). It also provides storage, access polices enforcement, and security operations. In many cases, CSP requires services from crowd contributors CC s for data collection, sharing, and mobile application support. It requires trust management for CC s to guard against malicious CC s and ensure security and privacy of data. However, end users or data requesters also demand reliable security solutions for sharing their data or accessing data from unknown CC s. To ensure strong security, mobile devices are not computationally feasible to perform complex cryptographic operations for desired privacy. To resolve these issues, we propose Reputation-aware Trust and Privacy Preservation scheme for MCC. In first phase, we deal with the trust management by utilizing reputation aware selection of CC s and leverage cloud services where CSP maintains trust score for CC s and data requesters. In second phase, we manage privacy preservation by using our proposed Anonymous Secure-shell Ciphertext-policy Attribute Based Encryption (AS-CABE). We have also proposed a hybrid policy tree mechanism for dynamic attribute selection used for security solutions and key management. Next, an anonymous secure shell is maintained between the CC s and the crowd servers to ensure registration after approval from trusted authority. In the similar vein, we propose outsourced encryption and decryption mechanism for mobiles that further utilize encryption and decryption service providers for complex operations. To the best of our knowledge, we are the first one to deal with the trust issues of data requester and privacy concerns of CC s and users both at the same time. After that, we have presented the security analysis to analyze AS-CABE against security attacks. Finally, the results are presented that ensure the supremacy of our proposed scheme as compared to counterparts in terms of reputation score, storage, computation, trust, resilience, encryption, and decryption time.

A vehicle in road networks shares location data with other vehicles and location-based services (LBS) through Internet-of-vehicles (IoV). By analyzing the location data from vehicles, LBS providers can offer vehicles better services. However, fake trajectories created by adversaries and malicious drivers diminish the location data utility in IoV and breach the quality of LBS. Illegal trajectory detection is vital to ensure location data utility in IoV. Existing location privacy-preserving schemes like obfuscation schemes add noise to actual location data increasing difficulties in detecting illegal trajectories. In this paper, we detect illegal trajectory in the case that all drivers in road networks protect location privacy by using obfuscation. We propose a new personalized obfuscation mechanism to dynamically and adaptively protect the location privacy of drivers in road networks. Considering uneven protection, we propose a trajectory detection scheme to classify trajectories in IoV. We evaluate our detection method with the data of real-world road networks, which is an important scenario of smart cities. The experiment results show that the proposed classifier outperforms existing studies in detecting malicious obfuscated trajectories with at least 94% of the Area Under the Curve (AUC) score.

The application of skyline queries on outsourced databases significantly aids online analysis, yet efficiently handling encrypted queries remains a formidable obstacle. Moreover, query outcomes are vulnerable to potential malicious cloud services. To circumvent these limitations, this work presents the <u>H</u>onest-<u>M</u>ajority and <u>M</u>aliciously <u>S</u>kyline <u>Q</u>uery scheme (HMMSQ), which facilitates efficient skyline queries while safeguarding the privacy of datasets, queries, and skylines, as well as detecting malevolent activities. The core of HMMSQ is an optimized skyline diagram constructed by a novel skyline region-splitting algorithm for accurate skyline queries. Furthermore, it mitigates the frequency of dataset accesses by leveraging a multi-path R-tree for secure skyline retrieval. Notably, the majority of malicious behavior detection is focused on the servers, thereby minimizing user authentication overhead. The complexity and security are thoroughly analyzed, and experimental evaluations on various datasets demonstrate its efficiency and practicality in terms of computational cost and communication overhead. Remarkably, HMMSQ outperforms existing methods in query latency, achieving up to an order of magnitude improvement.