Abstract:
Organizations now deal with massive amounts of data. Data
is collected from various points such as hospitals, credit card companies, and
search engines. After collecting this voluminous data, it is published and shared
for research. Data that is collected may have sensitive information that might
be used to identify an individual and consequently lead to privacy violations
when published. To address this challenge, privacy-preserving data publishing
(PPDP) seeks to remove threats to privacy while ensuring that the necessary
information is released for data mining. Various techniques have been proposed
to solve the problems associated with sensitive information. One such technique
is k-anonymity. This technique is the best and very efficient. However, it also
leads to loss of information, reduces data utility, and works well only with static
tables. In this paper, we proposed a technique that addresses the challenges of
K-anonymity known as the Bit-Coded-Sensitive Algorithm (BCSA). This algo
rithm is more efficient and effective and ensures that the privacy of the individual
is preserved by avoiding disclosure, and linkages and at the same time ensuring
the high quality and utility of data. BCSA first identifies the source of data and
based on that, uses bits to code sensitive data with a key.
