@article{MAKHILLJEAS2018131116322,
    title = {Personalized Privacy Preserving Incremental Data
Dissemination Through Optimal Generalization},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {13},
    number = {11},
    pages = {4205-4216},
    year = {2018},
    issn = {1816-949x},
    doi = {jeasci.2018.4205.4216},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2018.4205.4216},
    author = {S.,V. and},
    keywords = {optimal,high sensitive attribute,incremental data dissemination,personalization,Privacy,generalization,India},
    abstract = {A need to unveil health information for several reasons such as for health services, payment in case
of insurances, health care operations, research and so on is on high demand. Personal information is to be
disseminated without revealing the individual’s identity in all these circumstances. Tremendous work has been
carried out to provide privacy for publishing static data. Existing anonymization methods such as k-anonymity
and l-diversity models have led to a number of valuable privacy-protecting techniques for static data. This very
postulation implies a substantial limitation as in many applications data collection is rather a persistent process.
In places where data keeps on increasing on a daily basis, the current techniques are inadequate and suffer from
poor data quality and/or vulnerable to inferences. A very diminutive work has been carried out in this direction
and personalized privacy for incremental datasets has not been studied. In this study, we present a solution
that presents incremental data dissemination in the context of personalized privacy using optimal generalization.
An algorithm in incremental mode to handle personalized privacy issues with maximum diversity and minimum
anonymity is proposed. The experiments on continuously growing real world and synthetic datasets show that
the proposed scheme is efficient and produces publishable data of high utility.}
    }