Confidential Data Dispersion using Thresholding

With growing trend in "cloud computing" and increase in the data moving into the Internet, the need to store large amounts of data by service providers such as Google, Yahoo and Microsoft has increased over time. Now, more than ever, there is a need to efficiently and securely store large amounts of data. This thesis presents an implementation of a Ramp Scheme that confidentially splits a data file into a configurable number of parts or shares of equal size such that a subset of those shares can recover the data entirely. Furthermore, the implementation supports a threshold for data compromise and data verification to verify that the data parts have not been tampered with. This thesis addresses two key problems faced in large-scale data storage, namely, data availability and confidentiality.