Abstract
Recent interest in data collection and monitoring using data mining for security and business-related applications has raised privacy. Privacy Preserving Data Mining (PPDM) techniques require data modification to disinfect them from sensitive information or to anonymize them at an uncertainty level. This study uses PPDM with adult dataset to investigate effects of K-anonymization for evaluation metrics. This study uses Artificial Bee Colony (ABC) algorithm for feature generalization and suppression where features are removed without affecting classification accuracy. Also k-anonymity is accomplished by original dataset generalization.
Highlights
Data mining techniques extract knowledge to support various domains like marketing, medical diagnosis, weather forecasting and national security
Privacy Preserving Data Mining (PPDM) refers to data mining that tries to safeguard sensitive information from unsolicited/unsanctioned disclosure
Traditional data mining techniques analyze and model data set statistically in aggregation, while privacy preservation is about protecting against disclosure of individual data records
Summary
Data mining techniques extract knowledge to support various domains like marketing, medical diagnosis, weather forecasting and national security. Reduction in granularity leads to loss of data management or mining algorithms effectiveness, a trade-off between information loss and privacy Such techniques are: randomization method (Agrawal and Srikant, 2000; Agrawal and Aggarwal, 2002), k-anonymity model and l-diversity (Machanavajjhala et al, 2007), distributed privacy preservation and downgrading application effectiveness. These methods reduce risk of identification with public records, while reducing applications accuracy on transformed data (Aggarwal and Philip, 2008)
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