alexa Data Preserving By Anonymization Techniques for Collab
ISSN ONLINE(2319-8753)PRINT(2347-6710)

International Journal of Innovative Research in Science, Engineering and Technology
Open Access

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Special Issue Article

Data Preserving By Anonymization Techniques for Collaborative Data Publishing

R. Indhumathi1, S. Mohana Priya2
  1. PG Scholar, Dept. of CSE, MIET Engg. College, Tamil Nadu, India
  2. Assistant Professor, Dept. of CSE, MIET Engg. College, Tamil Nadu, India
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This paper mainly deals with the issue of privacy preserving in data mining while collaborating n number of parties and trying to maintain confidentiality of all data providers details while collaborating their database. Here two type of attacks are addressed “insider attack” and “outsider attack”. In insider attack, the data providers use their own records and try to retrieve other data provider details. Formal protection model k-Anonymity, l-diversity, t-closeness are used to protect privacy. Here notion of m-privacy algorithm is used to maintain privacy and secure multiparty computation protocol can also be used for privacy preserving.


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