The information leak of sensitive information on systems includes a serious threat to organization information security. Statistics from security companies, research and analysis establishments and government organizations shows the quantity of data-leak instances have increased speedily in recent years. The shortage of proper cryptography on files and communications due to human delusion is one amongst the most causes of data drop as shown by the statistics.
Deliberately planned attacks, unintended leaks and human mistakes cause most of the data-leak incidents.
Retransformations result in extremely unpredictable leak patterns. In this paper, the detecting advanced data-leak
patterns and sequence alignment technique is employed. For detecting long and inexact sensitive information patterns this algorithmic rule is meant. This detection is paired with a comparable sampling algorithmic rule. This comparable sampling algorithmic rule permits one to match the similarity of two differently sampled sequences. Smart detection accuracy in recognizing transformed leaks is achieved by this method. It implements a parallelized version of our algorithms in graphics process unit to attain high analysis information. Within the case of collective privacy preservation, organizations ought to address some fascinating conflicts. The advantage of our technique is that it allows the data and information owner to soundly delegate the detection operation to a semi honest supplier without revealing the sensitive data to the supplier. However, once organizations share information during a cooperative project, the goal isn't solely to protect in person recognizable data but conjointly sensitive information represented by some strategic patterns to possess the high multithread measurability of the data leak detection.
Real Time Impact Factor:
Pending
Author Name: R Usha, Pramod K, D Surbhi
URL: View PDF
Keywords: Information Leak Detection, Sensitive Data Patterns, Privacy, Security, Content Scrutiny
ISSN:
EISSN: 25827456
EOI/DOI: 10.51916/aksh.2020.v01i01.004
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