PROJECT TITLE:

Privacy-Preserving Detection of Sensitive Data Exposure - 2015

ABSTRACT:

Statistics from security companies, analysis establishments and government organizations show that the number of information-leak instances have grown rapidly in recent years. Among varied data-leak cases, human mistakes are one among the main causes of information loss. There exist solutions detecting inadvertent sensitive knowledge 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 data. Such an approach sometimes requires the detection operation to be conducted in secrecy. However, this secrecy requirement is difficult to satisfy in apply, as detection servers might be compromised or outsourced. In this paper, we tend to present a privacy-preserving knowledge-leak detection (DLD) solution to resolve the problem where a special set of sensitive data digests is utilized in detection. The advantage of our methodology is that it allows the data owner to securely delegate the detection operation to a semihonest supplier without revealing the sensitive information to the provider. We tend to describe how Internet service providers can provide their customers DLD as an add-on service with robust privacy guarantees. The analysis results show that our method will support correct detection with terribly little number of false alarms underneath various knowledge-leak eventualities.