TY  - JOUR
T1  - Personalized Privacy Preserving Incremental Data
Dissemination Through Optimal Generalization
AU - Ram Prasad Reddy, S. AU - Valli Kumari, V. AU - Raju, K.V.S.V.N. 
JO  - Journal of Engineering and Applied Sciences
VL  - 13
IS  - 11
SP  - 4205
EP  - 4216
PY  - 2018
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2018.4205.4216
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2018.4205.4216
KW  - optimal
KW  -high sensitive attribute
KW  -incremental data dissemination
KW  -personalization
KW  -Privacy
KW  -generalization
KW  -India
AB  - 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.
ER  -