FAANST: Fast Anonymizing Algorithm for Numerical Streaming DaTa Hessam Zakerzadeh (The University of Western Ontario, Canada), Sylvia L. Osborn (The University of Western Ontario, Canada). |
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A challenging task in privacy
protection for public data is to realize an algorithm that generalizes
a table according to a user's requirement. In this paper, we propose
an anonymization scheme for generating a k-anonymous table, and show
evaluation results using three different tables. Our scheme is based
on full-domain generalization and the requirements are automatically
incorporated into the generated table. The scheme calculates the
scores of intermediate tables based on user-defined priorities for
attributes and selects a table suitable for the user's requirements.
Thus, the generated table meets user's requirements and is employed in
the services provided by users without any modification or
evaluation.
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