Open AccessSELI: statistical evaluation based leaker identification stochastic scheme for secure data sharing
Author(s) -
Gupta Ishu,
Singh Ashutosh Kumar
Publication year - 2020
Publication title -
iet communications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.355
H-Index - 62
eISSN - 1751-8636
pISSN - 1751-8628
DOI - 10.1049/iet-com.2020.0168
Subject(s) - computer science , scheme (mathematics) , data mining , identification (biology) , spurious relationship , information leakage , computer security , machine learning , mathematics , mathematical analysis , botany , biology
Data leakage has become a critical challenge for any organisation, especially when the third party is involved with legitimate permissions to access its confidential data. This study provides a statistical evaluation‐based leaker identification (SELI) scheme to identify the malicious entity when the data is leaked by any agent at some unauthorised place. A new distribution strategy for object and agent selection which enriches the guilty agent recognition is introduced. The three algorithms named SELI–first come first serve, SELI–round Robin (SELI–RR) and SELI–shortest request first (SELI–SRF) are presented for the agent selection. Furthermore, the spurious data objects are added while distributing the data sets among the agents to uniquely identify the leaker. The SELI scheme estimates the guilty entity based on the allocated data among the agents using bigraph. The proposed solution achieves an improvement of up to 25.37 and 58.18% for average probability and average success rate respectively in case of SELI–SRF and 163.68% in case of SELI–RR for detection rate compared to the prior work. In addition to this, the SELI scheme proved its efficiency by securing up to 99.82% accuracy, 99.92% precision, 99.4% recall and 99.97% specificity.