Digest Authentication Method for Privacy Preserving In Anonymized Dataset

Data disclosure is more advantageous in an organization for achieving the privacy and security. The data can be micro data contain information about person, a business, or an organization. Statistics from security firms, research institutions and government organizations show that the number of data-leak instances has grown rapidly in recent years. Among various data-leak cases, human mistakes are one of the main causes of data loss. There exist solutions detecting inadvertent sensitive data leaks caused by human mistakes and to provide alerts for organizations. A common approach is to screen content in storage and transmission for exposed sensitive information in such a case a detection operation usually requires to be conducted in secrecy which becomes challenging to satisfy in practice, as detection servers may be compromised or outsourced. In this paper, we present a privacy preserving data-leak detection (DLD) solution to solve the issue where a special set of sensitive data digests is used in detection. The advantage of our method is that it enables the data owner to safely delegate the detection operation to a semi-honest provider without revealing the sensitive data to the provider. The proposed system is developed to provide the security for Personal Health Records. The data is anonymized and maintained by the administrator and the records are secured from the providers by hiding the sensitive data. Using our techniques, an Internet service provider (ISP) can perform detection on its customers’ traffic securely and provide data-leak detection as an add-on service for its customers. In another scenario, individuals can mark their own sensitive data and ask the administrator of their local network to detect data leaks for them.