In Preparation for Privacy Breach: Balancing Data Utility and Privacy Risk

Music City Center 

Microdata sharing is a common practice that enables various parties to benefit from the analysis of fine-grained information. However, it also poses a serious threat to the privacy of the individuals whose data are shared, as malicious actors may attempt to infer their sensitive attributes or identities from the shared data. To protect the privacy of the data subjects, data controllers often apply privacy-preserving techniques that introduce some distortion or noise to the original data, making it harder for an adversary to link them to specific individuals. However, these techniques also reduce the utility and accuracy of the data for legitimate analysis purposes. Therefore, a key challenge for data controllers is balancing the trade-off between privacy loss and data utility when choosing a suitable privacy-preserving technique. In this paper, we revisit a previously proposed method that allows partial recovery of the utility of randomized microdata by exploiting some auxiliary information. Based on this method, we propose a decision-making process that helps data controllers select the optimal differential privacy mechanism that minimizes privacy loss while meeting the utility requirement.

Data Privacy