Current applications tend to use personal sensitive information to achieve better quality with respect to their services. Since the third parties are not trusted the data must be protected such that individual data privacy is not compromised but at the same time operations on it would be compatible. The system implement, and evaluate a new privacy-preserving data-leak detection system that enables the data owner to safely deploy locally, or to delegate the traffic-inspection task to DLD providers without exposing the sensitive data. In our model, the data owner computes a special set of digests or fingerprints from the sensitive data, and then discloses only a small amount of digest information to the DLD provider. The exposure of sensitive data in storage and transmission poses a serious threat to organizational and personal security. Data leak detection aims at scanning content for exposed sensitive data. Because of the large content and data volume, such a screening algorithm needs to be scalable for a timely detection. In this project the system proposes data leak detection (DLD). It can be outsourced and be deployed in a semi-honest detection environment. This approach works well especially in the case where consecutive data blocks are leaked.