A Survey on Privacy Preserving Data Mining Techniques
Keywords:
privacy preserving, data mining, anonymization;, perturbation;Abstract
Privacy-preserving data mining (PPDM) has become a significant area of
interest for researchers, facilitating the sharing and analysis of sensitive information while
ensuring privacy protection. This paper investigates methods for maintaining data confidentiality
while retaining the critical attributes necessary for analysis. The authors assess
the efficacy of various PPDM techniques against criteria such as performance, data usability,
and levels of uncertainty. The key findings and limitations of each approach are
thoroughly reviewed and summarized. Various PPDM techniques present distinct advantages
alongside certain limitations: Anonymization guarantees the anonymity of data
owners but is vulnerable to linking attacks. Perturbation protects attributes independently
but does not allow for the reconstruction of original values from the altered data.
Randomization provides robust privacy protection but diminishes data utility due to the
introduction of noise. Cryptographic methods offer strong security and utility but tend to
be less efficient than other strategies. No single technique outperforms all criteria; rather,
each is more effective under particular circumstances. This paper delivers a comparative
analysis of PPDM techniques, emphasizing their strengths and weaknesses, and offers
insights into their applicability across different scenarios.
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