Anonymity enabled secure multi-party computation for indian BPO

In this Paper, we propose a new framework for secure multi-party computation that uses ambiguous user's identity. A huge amount of a potentially sensitive data exists in large a organization that needs to be mined. This raises concerns for people involved in the increased usage of data mining tools in both the public and private sectors. The utility to be gained from widespread data mining seems to come into direct conflict with an individual's need and right to privacy. A privacy preserving data mining solution aims at achieving the somewhat paradoxical property of enabling a data-mining algorithm to use data without ever actually "seeing" it. Thus, the benefits of data mining can be enjoyed, without compromising the privacy of concerned individuals. The very nature of huge amount of data during the mining needs to have such a framework with security. We propose the framework in the form a protocol for secure multiparty computation during data mining for BPO application. We also provide a suitable architecture that is tuned for this protocol to function.