Privacy Preserving Text Document Summarization
by A N Ramya Shree * 
, Kiran P
CSE Department, RNS Institute of Technology, Bengaluru,560098, India
* Author to whom correspondence should be addressed.
Journal of Engineering Research and Sciences, Volume 1, Issue 7, Page # 7-14, 2022; DOI: 10.55708/js0107002
Keywords: PHI, PPDP, Generalization, Sanitization
Received: 17 February 2022, Revised: 11 May 2022, Accepted: 23 June 2022, Published Online: 18 July 2022
APA Style
Shree, A. N. R., & P, K. (2022). Privacy Preserving Text Document Summarization. Journal of Engineering Research and Sciences, 1(7), 7–14. https://doi.org/10.55708/js0107002
Chicago/Turabian Style
Shree, A N Ramya, and Kiran P. “Privacy Preserving Text Document Summarization.” Journal of Engineering Research and Sciences 1, no. 7 (July 1, 2022): 7–14. https://doi.org/10.55708/js0107002.
IEEE Style
A. N. R. Shree and K. P, “Privacy Preserving Text Document Summarization,” Journal of Engineering Research and Sciences, vol. 1, no. 7, pp. 7–14, Jul. 2022, doi: 10.55708/js0107002.
Data Anonymization provides privacy preservation of the data such that input data containing sensitive information is converted into anonymized data. Hence, nobody can identify the information either directly or indirectly. During the analysis of each text document, the unique attributes reveal the identity of an entity and its private data. The proposed system preserves the sensitive data related to an entity available in text documents by anonymizing the sensitive documents either entirely or partially based on the sensitivity context which is very specific to a domain. The documents are categorized based on sensitivity context as sensitive and not-sensitive documents and further, these documents are subjected to Summarization. The proposed Privacy Preserving Text Document Summarization generates crisp privacy preserved summary of the input text document which consists of the most relevant domain-specific information related to the text document without defying an entity privacy constraints with the compression rate of 11%, the precision of 86.32%, and the recall of 84.28%.
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