🤖 AI Summary
This study addresses the central challenge in privacy-preserving classification: how to acquire useful individual information while avoiding the disclosure of sensitive attributes. For the first time, we formally integrate the privacy objective into the classification decision process, proving the resulting problem to be NP-complete. To tackle this, we propose an interactive querying framework based on generalized decision trees that optimizes question strategies to reduce uncertainty about the target attribute while constraining the leakage of private information. We develop an efficient heuristic solver combining greedy and genetic algorithms to navigate this trade-off. Experimental results demonstrate that our approach achieves an optimal balance between utility and privacy within acceptable computational time, offering a practical solution for non-intrusive personnel screening and customer data collection scenarios.
📝 Abstract
In this work we analyze the problem of, given the probability distribution of a population, questioning an unknown individual that is representative of the distribution so that our uncertainty about certain characteristics is significantly reduced—but the uncertainty about others, deemed private or sensitive, is not. Thus, the goal of the problem is extracting information being relevant to a legitimate purpose while preserving the privacy of individuals, which is crucial to enable non-intrusive selection processes in several areas. For instance, it is essential in the design of non-discriminatory personnel selection, promotion, and layoff processes in companies and institutions; in the retrieval of customer information being relevant to the service provided by a company (and no more); in certifications not revealing sensitive industrial information being irrelevant for the certification itself; etc. Interactive questioning processes are constructed for this purpose, which requires generalizing the notion of decision trees to account the amount of desired and undesired information retrieved for each branch of the plan. Our findings about this problem are both theoretical and practical: on the one hand, we prove its NP-completeness by a reduction from the Set Cover problem; and on the other hand, given this intractability, we provide heuristic solutions to find reasonable solutions in affordable time. In particular, a greedy algorithm and two genetic algorithms are presented. Our experiments indicate that the best results are obtained using a genetic algorithm reinforced with a greedy strategy.