# Safe Disassociation of Set-Valued Datasets

## TL;DR

This paper introduces safe disassociation, a method that enhances privacy in set-valued dataset anonymization by addressing the cover problem through partial suppression, extending k^m-anonymity.

## Contribution

It presents a novel safe disassociation technique that overcomes privacy issues in set-valued data anonymization, specifically tackling the cover problem with an efficient algorithm.

## Key findings

- The proposed method effectively prevents privacy breaches related to item coupling.
- Experiments demonstrate the efficiency and practicality of safe disassociation.
- The technique successfully extends k^m-anonymity to bucketized datasets.

## Abstract

Disassociation introduced by Terrovitis et al. is a bucketization based anonimyzation technique that divides a set-valued dataset into several clusters to hide the link between individuals and their complete set of items. It increases the utility of the anonymized dataset, but on the other side, it raises many privacy concerns, one in particular, is when the items are tightly coupled to form what is called, a cover problem. In this paper, we present safe disassociation, a technique that relies on partial-suppression, to overcome the aforementioned privacy breach encountered when disassociating set-valued datasets. Safe disassociation allows the $k^m$-anonymity privacy constraint to be extended to a bucketized dataset and copes with the cover problem. We describe our algorithm that achieves the safe disassociation and we provide a set of experiments to demonstrate its efficiency.