Privacy-preserving query processing (P 3 Q) techniques are increasingly important on partitioned databases, where relational queries have to be executed on horizontal data partitions held by different data owners. To conduct queries on the entire data partitions, the data owners may jointly collaborate to one another for sharing their private data or delegate them to an external service provider. In the literature these two solutions are referred to multi-party computation (MPC) and data outsourcing (DO), respectively. On the other hand, when no data owner or external service provider can be trusted enough to know all the inputs, privacy becomes a primary concern. To this purpose, data owners are not willing to share plaintext data with other parties or outsource plaintext to the service provider as well. A traditional solution to ensure privacy protection consists in adopting encryption scheme in order to help preventing information leakage. Such traditional solutions however reduce query execution efficiency notably in MPC scenario with large size data. This introduces the need to develop efficient techniques for P 3 Q, allowing data owners to respect data privacy when collaborating during the execution of queries. Recently, many techniques for P 3 Q have been developed in the multiparty context, which are based on the application of secure multi-party computation (SMC) protocols. While these solutions have focused on increasing the privacy, efficiency has been only marginally addressed. For this reason, in this thesis we describe a scalable approach for computing privacy-preserving queries on the entire relation(s) without sharing their private partitions. Our solution is applicable to a subset of SQL query language called SQL −− including selection and equi-join queries. In order to nicely scale with large size data, we show how computation and communication costs can be reduced via a novel bucketization technique. We consider the classical notion of query privacy, where the queries only learns as little as possible (in a computational sense) about the query. To ensure such privacy, our technique involves a trusted third party (TTP) only at the beginning of the protocol execution. Experimental results on horizontally partitioned, distributed data show the effectiveness of our approach. We also consider the problem of encrypted data outsourcing (EDO) where the owners encrypt their sensitive data with their own keys and outsource their partitions to a cloud service provider. This case poses a significant challenge to a cloud service provider, since the queries should be I would like to thank my brothers and sisters for their help and encouragement. They are always supportive on my choices. Last but not least, I would like to express my deep appreciation to my best friend, Marco Frasca, for his support in every possible way. He continuously pushed me to do my best and reinforced my strength in times when I doubted myself.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.