Paper Title
Minimizing Data Loss in Fraud Environment

A Data distributor has given precise data to a se t of supposedly trusted agents. Some of the data are leaked and found in an unjustified place. The distributor must assess the likelihood that the crevice data came from one or more agents, as opposed to having been individually gathered by other means. The authorize or unauthorized leakage of secret data is no doubt one of the most major security problems which organizations or systems face in this era. It also affects our personal day to day life: The personal information is available on social networks, or now-a-days it is also available on Smartphone is intentionally or unintentionally transferred to third party or hackers. Also a data distributor may give confidential data to some trusted agents or third parties. During this process some data is leaked or transferred to unauthorized place. We propose data allocation strategies that will give more probability of identifying leakages. We present a LIME data lineage framework for data flow across various locations. By using oblivious transfer, robust watermarking, and signature primitives we develop and analyze the data transfer protocol in a malicious environment between two entities. At the end of we perform an experimental result and analysis of our framework. We develop and analyze a novel accountable data transfer protocol between two entities within a malicious environment by building upon oblivious transfer, robust Watermarking, and signature primitives. Finally, we perform an experimental evaluation to demonstrate the practicality of our protocol and apply our framework to the important data leakage scenarios of data outsourcing and social networks. In general, we consider our lineage framework for data transfer, to be an key step towards achieving accountability by design. Index Terms- Data Leakage Prevention, Data Privacy Leakage Model Watermarking, Data Leakage Protection, Data Loss Prevention.