Enhanced Security for Two Party Confidential Data
Journal Title: International Journal of Engineering Sciences & Research Technology - Year 30, Vol 3, Issue 5
Abstract
Data mining is the process of extracting hidden predictive information from large databases to support decision making process. There may be a chance of revealing sensitive information stored in dataware house during extraction of hidden details. To protect this consequence a method is proposed to securely integrate person-specific sensitive data from two data providers, whereby the integrated data still retains the essential information for supporting data mining tasks. Most real-life scenarios are in need for simultaneous data sharing and privacy preservation of person sensitive data. Enhanced security for two party confidential data adopts differential privacy, provides a rigorous privacy model and makes no assumption about an adversary’s background knowledge. Two party algorithm is used in existing system for private data release for vertically-partitioned data between two parties in the semi-honest adversary model. A differentially-private mechanism is proposed to ensure that the probability of any output is equally likely from all nearly identical input data sets and thus guarantees that all outputs are insensitive to any individual’s data. Updating sensitivity scores is done to set scores for the sensitive details. Sha-2 algorithm is used for the purpose of user authentication to avoid masquerade attacks.
Authors and Affiliations
G. Sumathi*1
Keywords
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