Industrial Quality Prediction System through Data Mining Algorithm
Volume-3 | Issue-2
Comparative Analysis an Early Fault Diagnosis Approaches in Rotating Machinery by Convolution Neural Network
Volume-3 | Issue-2
Nakagami-m Fading Detection with Eigen Value Spectrum Algorithms
Volume-3 | Issue-2
Abstractive Summarization System
Volume-3 | Issue-4
Design of Adaptive Estimator for Nonlinear control system in Noisy Domain
Volume-3 | Issue-3
Automated Nanopackaging using Cellulose Fibers Composition with Feasibility in SEM Environment
Volume-3 | Issue-2
Comparative Analysis of Temperature Measurement Methods based on Degree of Agreement
Volume-3 | Issue-3
Transistor Sizing using Hybrid Reinforcement Learning and Graph Convolution Neural Network Algorithm
Volume-3 | Issue-3
A Review on Meshing Techniques in Biomedicine
Volume-3 | Issue-4
EL DAPP - An Electricity Meter Tracking Decentralized Application
Volume-2 | Issue-1
SMART STREET SYSTEM WITH IOT BASED STREET LIGHT OPERATION AND PARKING APPLICATION
Volume-1 | Issue-1
ENERGY AND POWER EFFICIENT SYSTEM ON CHIP WITH NANOSHEET FET
Volume-1 | Issue-1
Abstractive Summarization System
Volume-3 | Issue-4
A Review on Meshing Techniques in Biomedicine
Volume-3 | Issue-4
MIMO BASED HIGH SPEED OPTICAL FIBER COMMUNICATION SYSTEM
Volume-1 | Issue-2
Industrial Quality Prediction System through Data Mining Algorithm
Volume-3 | Issue-2
Comparative Analysis of Temperature Measurement Methods based on Degree of Agreement
Volume-3 | Issue-3
Transistor Sizing using Hybrid Reinforcement Learning and Graph Convolution Neural Network Algorithm
Volume-3 | Issue-3
VIRTUAL REALITY SIMULATION AS THERAPY FOR POSTTRAUMATIC STRESS DISORDER (PTSD)
Volume-1 | Issue-1
Comparative Analysis an Early Fault Diagnosis Approaches in Rotating Machinery by Convolution Neural Network
Volume-3 | Issue-2
Volume - 5 | Issue - 3 | september 2023
Published
22 September, 2023
The rapid proliferation of digital data has raised significant concerns regarding privacy and data security, necessitating the development of effective data anonymization techniques. This research presents a novel application of the Bat Algorithm (BA) for data anonymization, a nature-inspired optimization algorithm that mimics the echolocation behavior of Bats. The proposed approach leverages the BA's unique search capabilities to achieve a delicate balance between data utility and privacy preservation, a critical aspect in today's data-driven world. By treating data attributes as potential solutions and employing the BA's search process, the algorithm iteratively identifies and modifies sensitive attributes while minimizing information loss. This research contributes to the developing field of research on data anonymization by introducing a nature-inspired optimization technique that offers a promising alternative to traditional anonymization methods. Experimental results on various real-world datasets demonstrate the effectiveness of the proposed approach in achieving robust privacy protection while maintaining data quality, outperforming existing anonymization methods in terms of utility and computational efficiency. Furthermore, the proposed BA-based data anonymization approach exhibits versatility, scalability, and adaptability, making it suitable for diverse application domains, from healthcare and finance to social media and beyond. In summary, this study highlights the potential of the Bat Algorithm as a valuable tool in the field of data anonymization, offering a promising avenue for addressing the privacy challenges associated with the ever-expanding digital data landscape.
KeywordsBat Algorithm Anonymization Nature inspired Optimization Privacy
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