  Privacy and Fairness both are very important nowadays. For most of the cases
in the online service providing system, users have to share their personal
information with the organizations. In return, the clients not only demand a
high privacy guarantee to their sensitive data but also expected to be treated
fairly irrespective of their age, gender, religion, race, skin color, or other
sensitive protected attributes. Our work introduces a novel architecture that
is balanced among the privacy-utility-fairness trade-off. The proposed
mechanism applies Group Tagging Method and Fairly Iterative Shuffling (FIS)
that amplifies privacy through random shuffling and prevents linkage attack.
The algorithm introduces a fair classification problem by Relation Impact based
on Equalized Minimal FPR-FNR among the protected tagged group. For the count
report generation, the aggregator uses TF-IDF to add noise for providing
longitudinal Differential Privacy guarantee. Lastly, the mechanism boosts the
utility through risk minimization function and obtain the optimal
privacy-utility budget of the system. In our work, we have done a case study on
gender equality in the admission system and helps to obtain a satisfying result
which implies that the proposed architecture achieves the group fairness and
optimal privacy-utility trade-off for both the numerical and decision making
Queries.
