Secure kNN Query Processing in Untrusted Cloud Environments

Now days a Wireless devices which having geo-positioning facility like GPS enable users to give information about their current location. Users are interested in querying in their physical location like restaurants, college, home, etc. Such data may be important due to their information. Furthermore, storing such relevent information regularly to the users tedious task, so the author of such information will make the data access only to paying users. The users are send their proper location as the query parameter, and wish to accept as result the nearest position, i.e., nearest-neighbors (NNs). But actual data owners do not have the technical knowledge to support processed query on a large data, so they outsource information storage and querying to a main dataset. Many such cloud providers exist offer powerful storage and computational structures at less cost. However, such a dataset providers are not completely trusted, and typically behave in an cusial fashion. Specifically they uses the some rules to answer queries perfectly, but they also collect the locations of the users and the subscribers for other uses. Giving this information of locations can lead to security breaches and financial losses to the data provider, for whom the dataset is an important source of revenue. The importance of user locations leads to privacy and may refer subscribers from using the service altogether. In this paper, we propose a set of ideas that allow NN queries in an unsecured outsourced structure, while at the same time provide security to both the location and querying users' positions. Our ideas focuses on only secure order-preserving encryption method which is known to-date. We also provide performance measurements to reduce the processing cost inherent to processing on secured data, and we consider the problem of incrementally updating these datasets. We present an extensive performance measurement of our ideas to illustrate their use in practice.