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Privacy preserving reverse spatial and textual nearest neighbour query

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dc.contributor.author Dawar, Siddharth
dc.contributor.author Goyal, Vikram (Advisor)
dc.date.accessioned 2014-07-10T10:40:56Z
dc.date.available 2014-07-10T10:40:56Z
dc.date.issued 2014-07-10T10:40:56Z
dc.identifier.uri https://repository.iiitd.edu.in/jspui/handle/123456789/155
dc.description.abstract The rapid increase in popularity of location-based services have resulted in huge amount of spatial textual data being generated by applications like Foursquare, Facebook Places, Flickr etc. The location-based services o er convenience but threaten the location and query privacy of the user. The data collected by such servers can be to used study user behaviour or for stalking personal locations. A novel query which became popular in the past few years is Reverse k Nearest Neighbour Query (RkNN). Given a set of database objects O and a query point Q, the RkNN query returns those objects o 2 O, for which Q is one of its kth nearest neighbour, using an appropriately de ned similarity function on pairs of database objects. We propose a generalized framework for nding the reverse nearest neighbours of a query point which is independent of the underlying hierarchical indexing structure used as well as the used similarity measure. Our framework is independent of the type of database objects, but the only requirement is to de ne lower and upper bound similarity between any two object/groups of objects E and E0 of the given index structure and calculate the number of objects for every group of objects. We present two di erent approaches, namely, Lazy and Eager for performing monochromatic Reverse Nearest Neighbour query on spatial textual data. We conduct extensive experiments on real datasets and study the performance of both approaches. We address the problem of performing Reverse Nearest Neighbour (RkNN) search while preserving the location privacy of a user. Location Privacy can be preserved by anonymizing the location of a user using techniques like k-anonymity[1] or l-diversity[2]. The idea is to send a cloaked region to the server instead of the user's exact location so that location privacy is preserved. We formalize the problem of performing Reverse Nearest Neighbour Search on spatial objects when the exact location of database objects is not known to the server. A key challenge in performing such queries is to strike a balance between maintaining the correctness of results versus maintaining the privacy of a user. en_US
dc.language.iso en_US en_US
dc.subject Reverse Nearest Neighbour Query(RkNN) en_US
dc.subject Spatial-Textual data en_US
dc.subject IUR Tree en_US
dc.subject Location Privacy en_US
dc.subject Location-Based Services en_US
dc.title Privacy preserving reverse spatial and textual nearest neighbour query en_US
dc.type Thesis en_US

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