Small Domain Randomization: Same Privacy, More Utility

Random perturbation is a promising technique for privacy preserving data mining. It retains an original sensitive value with a certain probability and replaces it with a random value from the domain with the remaining probability. If the replacing value is chosen from a large domain, the retention probability must be small to protect privacy. For this reason, previous randomizationbased approaches have poor utility. In this paper, we propose an alternative way to randomize sensitive values, called small domain randomization. First, we partition the given table into sub-tables that have smaller domains of sensitive values. Then, we randomize the sensitive values within each sub-table independently. Since each sub-table has a smaller domain, a larger retention probability is permitted. We propose this approach as an alternative to classical partition-based approaches to privacy preserving data publishing. There are two key issues: ensure the published sub-tables do not disclose more private information than what is permitted on the original table, and partition the table so that utility is maximized. We present an effective solution.