An Efficient Modified Dolphin Echolocation Algorithm on Database Sanitizing for Hiding Sensitive Health Care Data
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Author:
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K. SIVAKUMAR
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Abstract:
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To protect the disclosure of sensitive knowledge by diminishing the trust/support of impressions by using the
disinfection data process that transforms the original in to modified database. Although the advantage of cloud
healthcare services, the correlated privacy problems are very concerned about individuals and governments.
Privacy risks increase when personal records of medical care are outsourced to the cloud based on the
sensitive nature of health information and the social and legal implications for its disclosure. Due to these
problems, Data Mining Preserve Privacy has become a critical problem in recent years. To overcome this
issue, we propose a Modified Dolphin Echolocation Algorithm (MDEA) for hide the sensitive health care data.
The main objective is to design a privacy-preserving outsourcing framework among the cloud model of hybrid.
Database sanitization is the process of transferring the original database to a disinfected database that hides
confidential health care data. Sensitive information are fetching from the original database by using the privacy
preserving outsourcing framework. From this sensitive information, the frequent item sets are generated and it
transfers to the FECT index tree. Hybrid clouds consist of public cloud as original database and public cloud as
sanitized database. Proposed Modified Dolphin Echolocation Algorithm utilized to sanitize the original
database. Substantive experiments will be conveyed out to compare the completion of the designed algorithm
with the most modern approaches in terms of side effects and similarity (integrity) of database. The
performance evaluation of presented method is compared with the existing GA, PSO, ACO and Firefly
framework. Our proposed method produce better results when compare to the other existing methods.
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Keyword:
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Database Sanitization, Modified Dolphin Echolocation Algorithm (MDEA), Privacy Preserving Outsourcing Framework.
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EOI:
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DOI:
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