Algorithm to satisfy l‐diversity by combining dummy records and grouping

Universities and corporations frequently use personal information databases for diverse objectives, such as research and marketing. The use of these databases inherently intersects with privacy issues, which have been the subject of extensive research. Traditional anonymization techniques predominantly focus on removing or altering identifiers and quasi‐identifiers (QIDs), the latter of which, although not unique, are closely correlated with individuals. However, this modification of QIDs can often impede data analysis. In this study, we introduce an innovative anonymization algorithm that combines the dummy‐record addition technique with a grouping method while circumventing the modification of QIDs. This fusion reduces the number of dummy records required for effective anonymization. The principal contribution of this study is the algorithm's ability to reduce the number of added dummy records. The proposed algorithm not only retains a high degree of data usefulness but also successfully adheres to the l$$ l $$‐diversity standard, which is a critical metric in privacy security. The experimental findings demonstrate that the proposed method offers a more equitable balance between safety and utility than existing technological solutions.