1st International ICST Conference on Communication System Software and MiddleWare

Research Article

Utilizing Network Features for Privacy Violation Detection

  • @INPROCEEDINGS{10.1109/COMSWA.2006.1665184,
        author={Jaijit  Bhattacharya and Rajanish  Dass and Vishal  Kapoor and S.K. Gupta},
        title={Utilizing Network Features for Privacy Violation Detection},
        proceedings={1st International ICST Conference on Communication System Software and MiddleWare},
  • Jaijit Bhattacharya
    Rajanish Dass
    Vishal Kapoor
    S.K. Gupta
    Year: 2006
    Utilizing Network Features for Privacy Violation Detection
    DOI: 10.1109/COMSWA.2006.1665184
Jaijit Bhattacharya1,2,*, Rajanish Dass3,4,*, Vishal Kapoor5,6,*, S.K. Gupta1,2,*
  • 1: Department of Computer Science and Engineering,
  • 2: Indian Institute of Technology, Delhi
  • 3: Computer and Information Systems Group,
  • 4: Indian Institute of Management Ahmedabad
  • 5: Oracle HP e-Governance,
  • 6: Center of Excellence, Gurgoan
*Contact email: jaijit@cse.iitd.ernet.in, rajanish@iimahd.ernet.in, vkapoor@cse.iitd.ernet.in, skg@cse.iitd.ernet.in


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