Utilizing Network Features for Privacy Violation Detection

Privacy, its violations and techniques to circumvent privacy violation have grabbed the centre-stage of both academia and industry in recent months. Corporations worldwide have become conscious of the implications of privacy violation and its impact on them and to other stakeholders. Moreover, nations across the world are coming out with privacy protecting legislations to prevent data privacy violations. Such legislations however expose organizations to the issues of intentional or unintentional violation of privacy data. A violation by either malicious external hackers or by internal employees can expose the organizations to costly litigations. In this paper, we propose PRIVDAM; a data mining based intelligent architecture of a privacy violation detection and monitoring system whose purpose is to detect possible privacy violations and to prevent them in the future. This paper elaborates on the use of network characteristics for differentiating between normal network traffic and potential malicious attacks. These attacks are usually hidden in common network services like http, ftp, udp etc. Experimental evaluations illustrate that our approach is scalable as well as robust and accurate in detecting privacy violations