A novel reconstruction attack on foreign-trade official statistics, with a Brazilian case study

TL;DR

This paper presents a new formalized transaction re-identification attack on Brazil's foreign-trade data, demonstrating its feasibility and potential privacy violations through extensive experiments and modeling as an NP-complete problem.

Contribution

It introduces a novel attack methodology formalized with quantitative information flow principles and models transaction re-identification as an ILP problem, highlighting privacy risks.

Findings

Successfully re-identified 2,003 transactions involving over $137M

Affected 348 Brazilian companies

Demonstrated the attack's applicability to similar international data sets

Abstract

In this paper we describe, formalize, implement, and experimentally evaluate a novel transaction re-identification attack against official foreign-trade statistics releases in Brazil. The attack's goal is to re-identify the importers of foreign-trade transactions (by revealing the identity of the company performing that transaction), which consequently violates those importers' fiscal secrecy (by revealing sensitive information: the value and volume of traded goods). We provide a mathematical formalization of this fiscal secrecy problem using principles from the framework of quantitative information flow (QIF), then carefully identify the main sources of imprecision in the official data releases used as auxiliary information in the attack, and model transaction re-construction as a linear optimization problem solvable through integer linear programming (ILP). We show that this problem is NP-complete, and provide a methodology to identify tractable instances. We exemplify the feasibility of our attack by performing 2,003 transaction re-identifications that in total amount to more than \$137M, and affect 348 Brazilian companies. Further, since similar statistics are produced by other statistical agencies, our attack is of broader concern.