Rupali Gangarde^1,2,*, Amit Sharma³, Ambika Pawar⁴

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 2171-2190, 2023, DOI:10.32604/cmc.2023.035559

Abstract Online Social Networks (OSN) sites allow end-users to share a great deal of information, which may also contain sensitive information, that may be subject to commercial or non-commercial privacy attacks. As a result, guaranteeing various levels of privacy is critical while publishing data by OSNs. The clustering-based solutions proved an effective mechanism to achieve the privacy notions in OSNs. But fixed clustering limits the performance and scalability. Data utility degrades with increased privacy, so balancing the privacy utility trade-off is an open research issue. The research has proposed a novel privacy preservation model using the enhanced clustering mechanism to overcome… More >

U. Selvi^*, S. Pushpa

Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1185-1196, 2022, DOI:10.32604/iasc.2022.020164

Abstract Due to the recent advancement in technologies, a huge amount of data is generated where individual private information needs to be preserved. A proper Anonymization algorithm with increased Data utility is required to protect individual privacy. However, preserving privacy of individuals whileprocessing huge amount of data is a challenging task, as the data contains certain sensitive information. Moreover, scalability issue in handling a large dataset is found in using existing framework. Many an Anonymization algorithm for Big Data have been developed and under research. We propose a method of applying Machine Learning techniques to protect and preserve the personal identities… More >