Image-based random rotation for preserving the data in data mining process

Image-based random rotation for preserving the data in data mining process

연구 분야: Databases

학회: Signal, Image and Video Processing

The privacy and security of big data have become a major concern in recent years, necessitating privacy-preserving data mining strategies to preserve the balance between data value and privacy. The application of data mining techniques to the web is known as web mining. The majority of consumers seek complete anonymity when using web apps and engaging in online activities, which raises privacy problems. Condensation, randomness, tree structure, and other traditional approaches are employed to maintain privacy. Existing techniques have limitations in that they are unable to balance data usefulness and may have privacy and scalability issues. Privacy-preserving tools such as encryption and machine learning techniques, among others, can be used to protect and classify the data stream. To overcome this, here, the data are transformed into image, and then, the random transformation is performed using enhanced particle swarm optimization. Here, the optimization is performed to identify the optimal random rotation of the data for both protection and better classification. The classification is performed through back propagation algorithm, and the perturbed data are tested against independent component analysis attack. The classification accuracy, computation time, and error rate of the classifier are measured, and it is compared with the existing method. The comparison result shows the achieved result of proposed method. This proposed system is done with the help of MATLAB 2021a.