Fuzz-classification (p, l)-Angel: An enhanced hybrid artificial intelligence based fuzzy logic for multiple sensitive attributes against privacy breaches

Journal article


Kanwal, T., Attaullaha, H., Anjum, A., Khan, A. and Jeon, G. 2022. Fuzz-classification (p, l)-Angel: An enhanced hybrid artificial intelligence based fuzzy logic for multiple sensitive attributes against privacy breaches. Elsevier Digital Communications and Networks. pp. 1-16. https://doi.org/10.1016/j.dcan.2022.09.025
AuthorsKanwal, T., Attaullaha, H., Anjum, A., Khan, A. and Jeon, G.
Abstract

The inability of traditional privacy-preserving models to protect multiple datasets based on sensitive attributes has prompted researchers to propose models such as SLOMS, SLAMSA, (p, k)-Angelization, and (p, l)-Angelization, but these were found to be insufficient in terms of robust privacy and performance. (p, l)-Angelization was successful against different privacy disclosures, but it was not efficient. To the best of our knowledge, no robust privacy model based on fuzzy logic has been proposed to protect the privacy of sensitive attributes with multiple records. In this paper, we suggest an improved version of (p, l)-Angelization based on a hybrid AI approach and privacy-preserving approach like Generalization. Fuzz-classification (p, l)-Angel uses artificial intelligence based fuzzy logic for classification, a high-dimensional segmentation technique for segmenting quasi-identifiers and multiple sensitive attributes. We demonstrate the feasibility of the proposed solution by modelling and analyzing privacy violations using High-Level Petri Nets. The results of the experiment demonstrate that the proposed approach produces better results in terms of efficiency and utility.

KeywordsGeneralization; Fuzzy-logic; Multiple Sensitive Attributes (MSA); Privacy disclosures ; Membership function
Year2022
JournalElsevier Digital Communications and Networks
Journal citationpp. 1-16
PublisherElseiver
ISSN2352-8648
Digital Object Identifier (DOI)https://doi.org/10.1016/j.dcan.2022.09.025
Web address (URL)https://www.sciencedirect.com/science/article/pii/S2352864822002000
Accepted author manuscript
File Access Level
Open
Publisher's version
License
File Access Level
Open
Output statusPublished
Publication dates
Online12 Oct 2022
Publication process dates
Accepted29 Sep 2022
Deposited16 Dec 2022
Permalink -

https://repository.derby.ac.uk/item/9vy93/fuzz-classification-p-l-angel-an-enhanced-hybrid-artificial-intelligence-based-fuzzy-logic-for-multiple-sensitive-attributes-against-privacy-breaches

Download files


Accepted author manuscript
1-s2.0-S2352864822002000-main.pdf
File access level: Open


Publisher's version
1-s2.0-S2352864822002000-main.pdf
License: CC BY-NC-ND
File access level: Open

  • 17
    total views
  • 15
    total downloads
  • 4
    views this month
  • 1
    downloads this month

Export as

Related outputs

Cohort-based kernel principal component analysis with Multi-path Service Routing in Federated Learning
Sikandar, H. S., Malik, S. R., Anjum, A., Khan, A. and Jeon, G. 2023. Cohort-based kernel principal component analysis with Multi-path Service Routing in Federated Learning. Future Generation Computer Systems. 149, pp. 518-530. https://doi.org/10.1016/j.future.2023.07.037
An Efficient and Privacy-preserving Blockchain-based Secure Data Aggregation in Smart Grids
Mahmood, A., Khan, A., Anjum, A., Maple, C. and Jeon, G. 2023. An Efficient and Privacy-preserving Blockchain-based Secure Data Aggregation in Smart Grids. Sustainable Energy Technologies and Assessments. 60, pp. 1-11. https://doi.org/10.1016/j.seta.2023.103414
Data Provenance in Healthcare: Approaches, Challenges, and Future Directions
Mansoor Ahmed (PhD), Amil Dar, Markus Helfert, Khan, A. and Jungsuk Kim 2023. Data Provenance in Healthcare: Approaches, Challenges, and Future Directions. Sensors. 23 (14), pp. 1-26. https://doi.org/10.3390/s23146495
Privacy Preservation in the Internet of Vehicles using Local Differential Privacy and IOTA Ledger
Iftikhar, Z., Anjum, A., Jeon, G., Shah, M. A. and Khan, A. 2023. Privacy Preservation in the Internet of Vehicles using Local Differential Privacy and IOTA Ledger. Springer Cluster Computing . pp. 1-17. https://doi.org/10.1007/s10586-023-04002-0
A Privacy-Enabled, Blockchain-Based Smart Marketplace
Bello Musa Yakubu, Majid Iqbal Khan, Khan, A., Adeel Anjum, Madiha Syed and Semeen Rehman 2023. A Privacy-Enabled, Blockchain-Based Smart Marketplace. Applied Sciences. 13 (5), pp. 1-16. https://doi.org/10.3390/app13052914
Blockchain-based DDoS attack mitigation protocol for device-to-device interaction in smart homes
Yakubu, M, Y., Khan, M. I., Khan, A., Jabeen, F. and Jeon, G. 2023. Blockchain-based DDoS attack mitigation protocol for device-to-device interaction in smart homes. Digital Communications and Networks. pp. 1-15. https://doi.org/10.1016/j.dcan.2023.01.013
Preserving Privacy of High-Dimensional Data by l-Diverse Constrained Slicing
Amin, Z., Anjum, A., Khan, A., Ahmad, A. and Jeon, G. 2022. Preserving Privacy of High-Dimensional Data by l-Diverse Constrained Slicing. Electronics. 11 (8), p. 1257. https://doi.org/10.3390/electronics11081257
Fault-Tolerant Secure Data Aggregation Schemes in Smart Grids: Techniques, Design Challenges, and Future Trends
Khan, H. M., Khan, A., Khan, B. and Jeon, G. 2022. Fault-Tolerant Secure Data Aggregation Schemes in Smart Grids: Techniques, Design Challenges, and Future Trends. MDPI Energies. 15 (24), p. 9350.. https://doi.org/10.3390/en15249350