Our research has developed a Parity-based Data Outsourcing (PDO) model. This model outsources a set of raw data by associating it with a set of parity data and then distributing both sets of data among a number of cloud servers that are managed independently by different service providers. Users query the servers for the data of their interest and are allowed to perform both authentication and correction. The former refers to the capability of verifying if the query result they receive is correct (i.e., all data items that satisfy the query condition are received, and every data item received is original from the data owner), whereas the latter, the capability of correcting the corrupted data, if any. Existing techniques all rely on complex cryptographic techniques and require the cloud server to build verification objects. In particular, they support only query authentication, but not error correction. In contrast, our approach enables users to perform both query authentication and error correction, and does so without having to install any additional software on a cloud server, which makes it possible to take advantage of the many cloud data management services available on the market today.

This thesis makes the following contributions. 1) We extend the PDO model, which was originally designed for one-dimensional data, to handle multi-dimensional data. 2) We implement the PDO model, including parity coding, data encoding, data retrieval, query authentication and correction. 3) We evaluate the performance of the PDO model. We compare it with Merkle Hash Tree (MH-tree) and Signature Chain, two existing techniques that support query authentication, in terms of storage, communication, and computation overhead.