Secure and private outsourcing to untrusted cloud servers

One major impediment to large-scale use of cloud services is concern for confidentiality of the data and the computations carried out on it. This dissertation advances the state of art for secure and private outsourcing to untrusted cloud servers by solving three problems in the computational outsourcing setting and extending the semantics of oblivious storage in the storage outsourcing setting. In computational outsourcing, this dissertation provides protocols for two parties to collaboratively design engineering systems and check certain properties of the codesigning system with the help of a cloud server, without leaking the designing parameters to each other or to the server. It also provides approaches to outsource two computationally intensive tasks, image feature extraction and generalized matrix multiplication, preserving the confidentiality of both the input data and the output result. Experiments are included to demonstrate the viability of the protocols. In storage outsourcing, this dissertation extends the semantics of the oblivious storage scheme by providing algorithms to support nearest neighbor search. It enables clients to perform nearest neighbor queries on the outsourced storage without leaking the access pattern