DEVELOPMENT OF DATA LEAKAGE DETECTION

International Journal of Computer Science (IJCS) Published by SK Research Group of Companies (SKRGC).

Abstract

Our goal is to detect when the distributor’s sensitive data has been leaked by interjected, and if possible, to spot the agent that leaked the info. an information distributor has given sensitive data to a collection of supposedly reliable agents. Sometimes data is leaked and located in unconstitutional place. as an example, a hospital may give patient records to researchers who will devise new treatments. Similarly, a corporation may have partnerships with other companies that need sharing customer data. Another enterprise may outsource its processing, so data could be given to varied other companies. The owner of the info is named as distributors and therefore the trusted third parties are called as agents. Data leakage happens a day when confidential business information like customer or patient data, company secrets, budget information etc. Are leaked out. When this information is leaked out, then the businesses are at serious risk. Most likely data are being leaked from agent’s side. So, companies need to very careful while distributing such knowledge to agents. The Goal of Our project is to investigate “how the distributer can allocate the confidential data to the Agents in order that the leakage of information would be minimized to a Greater Extent by finding a guilty agent”.

References

[1] X. Shu, et al., “Fast Detection of Transformed Data Leaks,” in IEEE Transactions on Information Forensics and Security, vol. 11, no. 3, pp. 528-542, 2016. [2] X. Shu, et al., “Privacy-preserving detection of sensitive data exposure,” IEEE Trans. Inf. Forensics Security, vol. 10, no. 5, pp. 1092-1103, 2015. [3] F. Liu, et al., “Privacy-preserving scanning of big content for sensitive data exposure with MapReduce,” in Proc. 5th ACM Conf. Data Appl. Secur. Privacy (CODASPY), pp. 195-206, 2015. [4] Nadkarni and W. Enck, “Preventing accidental data disclosure in modern operating systems,” in Proc. 20th ACM Conf. Comput. Commun. Secur., pp. 1029-1042, 2013. [5] R. Hoyle, et al., “Attire: Conveying information exposure through avatar apparel,” in Proc. Conf. Comput. Supported Cooperat. Work Companion (CSCW), pp. 19-22, 2013. [6] H. A. Kholidy, et al., “DDSGA: A data-driven semi-global alignment approach for detecting masquerade attacks,” IEEE Trans. Dependable Secure Comput., vol. 12, no. 2, pp. 164-178, 2015.

Keywords

Patient Data, Sensitive Data, Agents, Data Leakage.