# A. membership function. S – Shaped fuzzy membership function

A. Fuzzy
Based Approach:

The concepts of fuzzy sets are merely an extension to the generic set theory. In fuzzy sets the
of data is
done using a
suitable fuzzy membership  function fuzzification  of
original  data
to
fuzzy set  preserves  privacy and  relativity between data9. This approach enhances the efficiency of clustering by
decreasing required
number of passes. The fuzzy membership function used also influences the processing time. Thus, selecting proper fuzzy
membership function can improve the efficiency of algorithm
and also aid in overcoming most of the limitations
stated in the previous section. In our work, data masking
is achieved using S-Shaped membership function. S –
Shaped fuzzy membership function is given
by

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……..(1)

Where x – is value of the sensitive attribute, a &
b –
is minimum and maximum value in the sensitive attribute. The only
limitation of this approach is that it can only map the
values between 0 and 1. Still, it can be used to mask the data having
their domain from 0 to 1.

B.
Rail-fence Method :

This technique is mostly
applied to categorical data where in the original data is written
row/column-wise and the transformed data is fetched by traversing along column/row wise respectively.

C.
Map Range (Rosetta
Code) :

Map  Range  method  of  normalization
performs
mapping  of  original
data  to  a  range (mostly for mapping large values to a small range) given by the user. This method is used for numerical data. The formula is given as follows: Given two ranges, a1, a2 and b1, b2; then a value s in range a1, a2 is linearly mapped to a value t in range b1, b2 when:

…….………………………………(2)

D.