Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: New privacy regulations together with ever increasing data availability and computational por have created a huge interest in data privacy research. One major research direction is built around  k -anonymity property, which is required for the released data. Although many  k -anonymization algorithms exist for static data, a complete framework to cope with data evolution (a real world scenario) has not been proposed before. ,  introduce algorithms for the maintenance of  k -anonymized versions of large evolving datasets. These algorithms incrementally manage insert/delete/update dataset modifications. Our results shod that incremental maintenance is very efficient compared with existing techniques and preserves data quality. The second main contribution of this paper is an optimization algorithm that is able to improve the quality of the solutions attained by either the non-incremental or incremental algorithms.
['Traian Marius Truta', 'Alina Campan']
I am writing a research paper on a idea K -anonymization incremental maintenance and optimization techniques
[{"from": "human", "value": "I am writing a research paper on a idea K -anonymization incremental maintenance and optimization techniques"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: New privacy regulations together with ever increasing data availability and computational por have created a huge interest in data privacy research. One major research direction is built around  k -anonymity property, which is required for the released data. Although many  k -anonymization algorithms exist for static data, a complete framework to cope with data evolution (a real world scenario) has not been proposed before. ,  introduce algorithms for the maintenance of  k -anonymized versions of large evolving datasets. These algorithms incrementally manage insert/delete/update dataset modifications. Our results shod that incremental maintenance is very efficient compared with existing techniques and preserves data quality. The second main contribution of this paper is an optimization algorithm that is able to improve the quality of the solutions attained by either the non-incremental or incremental algorithms."}]