Secure kNN Query Processing in Untrusted Cloud Environments: an Overview

ABSTRACT: Nowadays, data are stored to a third party in cloud environments and query processing is also done by the third party to reduce the expense to maintain the system. Although there are lots of advantages in using independent third parties in query processing, security problems become more crucial since we cannot completely trust the third parties which can be easily corrupted or malfunctioning. The security problems with untrusted third parties are multifaceted in several areas such as privacy, authentication, and recovery. For privacy, the third party should not be able to know what the user's query is since the query itself describes the user's interest. For authentication, the user should be able to verify that the information from the third party is not tampered since the correctness of the query results depends upon the correctness of the information from the third party. For recovery, when the result is found to be forged by an adversary, we should be able to find the adversary and get a correct result by removing the adversary. To address these challenges, we propose several schemes. First, with respect to secure kNN query processing and secure proximity detection, we give novel schemes based on Mutable Order Preserving Encryption (MOPE) and Secure Point Evaluation Method (SPEM). Second, for authenticated top-k aggregation, we suggest novel schemes using Three Phase Uniform Threshold Algorithm, Merkle Hash Tree, and Condensed-RSA. Third, for detecting malicious nodes, we propose novel algorithms based on Additively Homomorphic Encryption and Multipath Transmission. Our experimental evaluation and security analyses demonstrate that robust mechanisms can be deployed with a minimal amount of computational and communicational expense

Secure kNN Query Processing using VD-kNN In an Untrusted Cloud Enviroments

At the moment, data is stored with a third party in cloud environments and query processing is also done by third party to reduce the amount to maintain the system. Mobile devices with geo-positioning capabilities register users to access information that is applicable to their present and current location. Users are interested in querying about points of interest (POI) in their physical concurrence. e.g. restaurants, cafes, gas station, etc. Objects specialized in various areas of interest (e.g., entertainment, and travel) gather large amounts of geotagged data that appeal to register users. Such data may be observant due to their substance. Furthermore, keeping such information latest form and applicable to the users is not an easy task , so the holder of such datasets will make the data available only to paying customers. Users send their current location as the query parameter and wish to receive as result the nearest POIs, i.e., nearest-neighbors (NNs). Keywords —Query services; kNN query; structural databases; mutable order preserving encoding