C-mixture and Multi-constraints Based Genetic Algorithm for Collaborative Data Publishing
Yogesh Kulkarni, Senthil Murugan
Journal of Information & Systems Management, Vol. 7, No. 4, 2017.
DOI: 
Link: http://www.dline.info/jism/fulltext/v7n4/jismv7n4_2.pdf
Abstract:
Due to the increasing need of using distributed databases, a high demand on sharing data to easily update
and access the useful information without any interruption. The sharing of distributed databases cause a serious
issue of securing information since the databases consists of sensitive personal information. To preserve the sensitive
information and at the same time, releasing the useful information, a significant effort is made by the researchers
under privacy preserving data publishing that have been received considerable attention in recent years.In this work,
a new privacy measures, called c-mixture is introduced to maintain the privacy constraint without affecting utility of
the database. In order to apply the proposed privacy measure to privacy preserving data publishing, a new algorithm
called, CPGEN is developed using genetic algorithm and multi-objective constraints. The proposed multi-objective
optimization considered the multiple privacy constraints along with the utility measurement to measure the importance.
Also, the proposed CPGEN is adapted to handle the cold-start problem which is commonly happened in distributed
databases. The proposed algorithm is experimented with adult dataset and quantitative performance is analyzed using
generalized information loss and average equivalence class size metric. From the experimentation, we proved that the
proposed algorithm maintained the privacy and utility as compared with the existing algorithm.