A Confidentiality Preserving Data Leaker Detection Model for Secure Sharing of Cloud Data using Integrated Techniques
Ishu Gupta, Ashutosh Kumar Singh · 2019
Leakage of critical information through intentional or unintentional disclosure by the malicious entities to the aggrieved third party poses one of the most serious security hazards to various organizations. In the real world scenario, a data distributor has to share confidential data among various stakeholders. It comprises a number of threats in transferring the critical data, as it can be made public by any disgruntled employee or the indignant entity. Data Leakage Problem (DLP) has become a critical challenge and it keeps on increasing day by day. It is crucial to protect the critical data as it can be misused by any vicious entity. To resolve DLP, we present a generic Data Leaker Detection Model (DLDM), which identifies the malicious entity responsible for data leak. The proposed model ensures the data confidentiality and preserves the security of the sensitive information via applying an integration of cryptography, watermarking and hashing techniques. The results show that the computation times in the complete process are 2285.31 ms and 5628.84 ms when 200 documents of size 20MB are provided to the single user and distinct users respectively.