Probabilistic Anonymization for Achieving Data Privacy

Provided by: International Journal of Advanced Research in Computer Science and Software Engineering (IJARCSSE)
Topic: Data Management
Format: PDF
The increased availability of individual data combined with today's significant computational power and the tools available to analyze this data have created major privacy concerns not only for researchers but also for the public and legislators. Every organization sends the data to researchers for the mining purpose. This may affect the privacy of an individual. There are different privacy measures to protect the individual's data. One of the privacy measures is k-anonymity. The k-anonymity property does not protect the data against attribute disclosure. The p-sensitive k-anonymity property overcomes this problem. But it is insufficient to prevent similarity attack.

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