1,067 research outputs found

    AUTHENTICATION OF K NEAREST NEIGHBOR QUERY ON ROAD NETWORKS

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    ABSTRACT This work specifically focus on the k-nearest-neighbor (kNN) query verification on road networks and design verification schemes which support both distance verification and path verification. That is the k resulting objects have the shortest distances to the query point among all the objects in the database, and the path from the query point to each knearest-neighbor result is the valid shortest path on the network. In order to verify the kNN query result on a road network, a naïve solution would be to return the whole road network and the point of interest (POI) dataset to the client to show correctness and completeness of the result

    Shortest Path Computation with No Information Leakage

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    Shortest path computation is one of the most common queries in location-based services (LBSs). Although particularly useful, such queries raise serious privacy concerns. Exposing to a (potentially untrusted) LBS the client's position and her destination may reveal personal information, such as social habits, health condition, shopping preferences, lifestyle choices, etc. The only existing method for privacy-preserving shortest path computation follows the obfuscation paradigm; it prevents the LBS from inferring the source and destination of the query with a probability higher than a threshold. This implies, however, that the LBS still deduces some information (albeit not exact) about the client's location and her destination. In this paper we aim at strong privacy, where the adversary learns nothing about the shortest path query. We achieve this via established private information retrieval techniques, which we treat as black-box building blocks. Experiments on real, large-scale road networks assess the practicality of our schemes.Comment: VLDB201

    Secure Mix-Zones for Privacy Protection of Road Network Location Based Services Users

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    Sensor Proxy Mobile IPv6 (SPMIPv6)—A Novel Scheme for Mobility Supported IP-WSNs

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    IP based Wireless Sensor Networks (IP-WSNs) are gaining importance for their broad range of applications in health-care, home automation, environmental monitoring, industrial control, vehicle telematics and agricultural monitoring. In all these applications, mobility in the sensor network with special attention to energy efficiency is a major issue to be addressed. Host-based mobility management protocols are not suitable for IP-WSNs because of their energy inefficiency, so network based mobility management protocols can be an alternative for the mobility supported IP-WSNs. In this paper we propose a network based mobility supported IP-WSN protocol called Sensor Proxy Mobile IPv6 (SPMIPv6). We present its architecture, message formats and also evaluate its performance considering signaling cost, mobility cost and energy consumption. Our analysis shows that with respect to the number of IP-WSN nodes, the proposed scheme reduces the signaling cost by 60% and 56%, as well as the mobility cost by 62% and 57%, compared to MIPv6 and PMIPv6, respectively. The simulation results also show that in terms of the number of hops, SPMIPv6 decreases the signaling cost by 56% and 53% as well as mobility cost by 60% and 67% as compared to MIPv6 and PMIPv6 respectively. It also indicates that proposed scheme reduces the level of energy consumption significantly

    Continuous spatial query processing over clustered data set

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    There exists an increasing usage rate of location-based information from mobile devices, which requires new query processing strategies. One such strategy is a moving (continuous) region query in which a moving user continuously sends queries to a central server to obtain data or information. In this thesis, we introduce two strategies to process a spatial moving query over clustered data sets. Both strategies utilize a validity region approach on the client in order to minimize the number of queries that are sent to the server. We explore the use of a two-dimensional indexing strategy, as well as the use of Expectation Maximization (EM) and k-means clustering. Our experiments show that both strategies outperform a Baseline strategy where all queries are sent to the server, with respect to data transmission, response time, and workload costs
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