Vol. 3 No. 2 (2024): April
RESEARCH ARTICLES

Data Leakage and Detection

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  • Prathik B K,
  • Manovikyath L,
  • Sandeep Y,
  • Aryaman,
  • Abhiram P
Prathik B K
School of Computer Science and Engineering, REVA University, Bengaluru, India
Manovikyath L
School of Computer Science and Engineering, REVA University, Bengaluru, India
Sandeep Y
School of Computer Science and Engineering, REVA University, Bengaluru, India
Aryaman
School of Computer Science and Engineering REVA University, Bengaluru, India
Abhiram P
School of Computer Science and Engineering REVA University, Bengaluru, India
DOI: https://doi.org/10.5281/zenodo.11065679

Published 2024-05-13

Keywords

  • Campus Network,
  • Cisco Packet Tracer,
  • Network Design,
  • Quality of Service,
  • Wireless Integration,
  • Management and Monitoring
    • ...More
    Less

How to Cite

Prathik B K, Manovikyath L, Sandeep Y, Aryaman, & Abhiram P. (2024). Data Leakage and Detection. International Journal of Computational Learning & Intelligence, 3(2), 318–322. https://doi.org/10.5281/zenodo.11065679

Copyright (c) 2024 Prathik B K, Manovikyath L, Sandeep Y, Aryaman, Abhiram P

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Abstract

 

We study the following problem: A data distributor has given sensitive data to a set of supposedly trusted agents (third parties). Some of the data are leaked and found in an unauthorized place (e.g., on the web or somebody's laptop). The distributor must assess the likelihood that the leaked data came from one or more agents, as opposed to having been independently gathered by other means. We propose data allocation strategies (across the agents) that improve the probability of identifying leakages. These methods do not rely on alterations of the released data (e.g., watermarks). In some cases, we can also inject “realistic but fake” data records to further improve our chances of detecting leakage and identifying the guilty party “realistic but fake” data records to further improve our chances of detecting leakage and identifying the guilty party.
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References

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