A Review on Privacy Preserving Data Mining Approaches


  • Anu Thomas Asst.Prof. Computer Science & Engineering Department Gujarat Technological University
  • Jimesh Rana Asst.Prof. Computer Science &Engineering Department DJMIT,Mogar,Anand Gujarat Technological University




The field of privacy has seen rapid advances in
recent years because of the increase in the ability to store
data. In particular, recent advances in the data mining field
have lead to increased concerns about privacy. While the
topic of privacy has been traditionally studied in the context
of cryptography and information hiding, recent emphasis on
data mining has lead to renewed interest in the field.
A fruitful direction for future data mining research will be the
development of techniques that incorporate privacy concerns.
Specially, we address the following question. Since the primary
task in data mining is the development of models about
aggregated data, can we develop accurate models without access
to precise information in individual data records? We consider
the concrete case of building a decision-tree classier from
training data in which the values of individual records have been
perturbed. The resulting data records look very different from the
original records and the distribution of data values is also very
different from the original distribution. While it is not possible to
accurately estimate original values in individual data records, we
propose a novel reconstruction procedure to accurately estimate
the distribution of original data values. By using these
reconstructed distributions, we are able to build classifiers whose
accuracy is comparable to the accuracy of classifiers built with
the original data.



How to Cite

Anu Thomas, & Jimesh Rana. (2022). A Review on Privacy Preserving Data Mining Approaches. International Journal of Advance Engineering and Research Development (IJAERD), 2(13), -. Retrieved from http://ijaerd.com/index.php/IJAERD/article/view/5685