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
