Disclosure Risk From Homogeneity Attack in Differentially Privately Sanitized Frequency Distribution

Differential privacy (DP) provides a robust model to achieve guarantees for released information. We examine the protection potency of sanitized multi-dimensional frequency distributions via DP randomization mechanisms against homogeneity attack (HA). HA allows adversaries obtain exact values on sensitive attributes their targets without having identify them from data. propose measures disclosure risk and derive closed-form relationships between loss parameters in HA. The availability assists understanding abstract concepts by putting context concrete offers perspective choosing when employing information sanitization release practice. apply mathematical real-life datasets demonstrate assessment due differentially private at various parameters.