Generous Information Safety System for Investors in Online Trading Secretly using KP-ABE Machine Learning Method

Authors

  • Srinivas Kolli Assistant professor, Department of information technology, Vallurupalli nageswara rao vignana Jyothi Institute of engineering & technology, Vignana jyothi nagar, pragathi nagar, Nizampet (s.o), hyderabad, telangana-500090, India.
  • Akundi Sai Hanuman Professor of CSE , Gokaraju Lailavathi womens engineering college, Hyderabad
  • Javvaji V. K. Ratnam Professor Narasaraopeta Engineering College, Narasaraopet, AP , India,
  • J. N. S. S. Janardhana Naidu Department of CSE, Vishnu Institute of Technology, Vishnupur, Bhimavaram, Andhra Pradesh-534202
  • D. Shankar Department of CSE,Vishnu Institute of Technology, Vishnupur, Bhimavaram, Andhra Pradesh-534202
  • K. Saikumar School of engineering, department of CSE, Malla Reddy University, Maisammaguda, Dulapally, Hyderabad, Telangana 500043

Keywords:

Secure Pub/inv, Investors’ Privacy, Publicists’ Confidentiality, Collusion Resistance

Abstract

Publishers and investors (pub/inv) have agreed on a framework for the sharing of information as part of a brokering network. However, malicious brokers or brokers who are curious about the data may hack into the system and take the information. This is a suitable way for safeguarding sensitive information and data security subscriptions until brokers begin to disseminate it. To prevent revealing subscriptions and publications, modern techniques need brokers to employ encrypted connections. Even if the preferences of evil publications are protected, the data of innocent subscribers will be hacked, even if they are protected by malicious brokers. When pessimistic brokers are present, the programmed provides a publishing and subscription service that ensures publication and subscription anonymity. As a result, assaults on the KP-ABE algorithms by unskilled traders or antagonistic publishers are futile.

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Published

05.12.2023

How to Cite

Kolli, S. ., Hanuman, A. S. ., Ratnam, J. V. K. ., Naidu, J. N. S. S. J. ., Shankar, D. ., & Saikumar, K. . (2023). Generous Information Safety System for Investors in Online Trading Secretly using KP-ABE Machine Learning Method. International Journal of Intelligent Systems and Applications in Engineering, 12(7s), 285–297. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4072

Issue

Vol. 12 No. 7s (2024)

Section

Research Article

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