A review and analysis of privacy preserving detection techniques of sensitive data exposure

Statistics from the different security firms, research institutions and government organizations show that there is a rapid growth in the numbers of data-leak instances in recent years. Among different data-leak cases, human mistakes are one of the main causes of data loss. There exist alternate solutions in detecting inadvertent sensitive data leaks caused by human mistakes and to provide alerts for organizations. One of the common approaches is to screen content in storage and transmission for exposed sensitive information. Such an approach usually requires secrecy in conducting the detection operation. However, this secrecy requirement is challenging to satisfy in practice, as detection servers may be compromised or outsourced. In this proposed research work, 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 this 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. It is also possible, how Internet service providers can offer their customers DLD as an add-on service with strong privacy guarantees. This proposal will help to support accurate detection with very less number of false alarms under different data-leak scenarios.