Privacy-preserving big data publication: (k, l) anonymity

J. Andrew, J. Karthikeyan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


The explosion in variety and volume of information in the public domains provides an enormous opportunity for analysis and business purposes. Availability of private information is of explicit interest in sanctionative highly tailored services tuned to individual desires. Though this is highly favorable to the individuals, the conventional anonymization techniques still possess threats to the privacy of individuals through reidentification attacks. The focus of this paper is to propose a privacy-preserving approach called (K, L) Anonymity that combines k-anonymity and Laplace differential privacy techniques. This coherent model guarantees privacy from linkage attacks as the risk is mitigated through experimental results. The proposed model also addressed the shortcomings of other traditional privacy-preserving mechanisms and validated with publicly available datasets.

Original languageEnglish
Title of host publicationIntelligence in Big Data Technologies—Beyond the Hype - Proceedings of ICBDCC 2019
EditorsJ. Dinesh Peter, Steven L. Fernandes, Amir H. Alavi, Amir H. Alavi
PublisherSpringer Gabler
Number of pages12
ISBN (Print)9789811552847
Publication statusPublished - 2021
Event3rd International Conference on Big-Data and Cloud Computing, ICBDCC 2019 - Coimbatore, India
Duration: 06-12-201907-12-2019

Publication series

NameAdvances in Intelligent Systems and Computing
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365


Conference3rd International Conference on Big-Data and Cloud Computing, ICBDCC 2019

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science(all)


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