{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T12:37:20Z","timestamp":1775738240946,"version":"3.50.1"},"reference-count":28,"publisher":"World Scientific Pub Co Pte Ltd","issue":"Supp01","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Inter. Net."],"published-print":{"date-parts":[[2022,3]]},"abstract":"<jats:p> An optimal approach to anonymization using small data is proposed in this study. Map Reduce is a big data processing framework used across distributed applications. Prior to the development of a map reduce framework, data are distributed and clustered using a hybrid clustering algorithm. The algorithm used for grouping together similar techniques utilises the k-means clustering algorithm, along with the MFCM clustering algorithm. Clustered data is then fed into the map reduce frame work after it has been clustered. In order to guarantee privacy, the optimal k anonymization method is recommended. When using generalisation and randomization, there are two techniques that can be employed: K-anonymity, which is unique to each, depends on the type of the quasi identifier attribute. Our method replaces the standard k anonymization process by employing an optimization algorithm that dynamically determines the optimal k value. This algorithm uses the Modified Grey Wolf Optimization (MGWO) algorithm for optimization. The memory, execution time, accuracy, and error value are used to assess the recommended method\u2019s practise. This experiment has shown that the suggested method will always finish ahead of the existing method by using the least amount of time while ensuring the greatest level of security. The current technique gets the lowest accuracy and the privacy proposed achieves the maximum accuracy while compared to the current technique. The solution is implemented in Java with Hadoop Map-Reduce, and it is tested and deployed in the cloud on Google Cloud Platform. <\/jats:p>","DOI":"10.1142\/s0219265921410395","type":"journal-article","created":{"date-parts":[[2022,2,10]],"date-time":"2022-02-10T09:05:53Z","timestamp":1644483953000},"source":"Crossref","is-referenced-by-count":26,"title":["Privacy Preserving with Modified Grey Wolf Optimization Over Big Data Using Optimal K Anonymization Approach"],"prefix":"10.1142","volume":"22","author":[{"given":"S.","family":"Sai Kumar","sequence":"first","affiliation":[{"name":"Department of IT, PVP Siddhartha Institute of Technology, Vijayawada, Andhra Pradesh, India"}]},{"given":"Anumala Reethika","family":"Reddy","sequence":"additional","affiliation":[{"name":"Department of CSE, Vignan\u2019s Institute of Information Technology, Visakhaptnam 530049, AP, India"}]},{"given":"B. Sivarama","family":"Krishna","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Lakireddy Bali Reddy College of Engineering, AP 521230, India"}]},{"given":"J. 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