A New Algorithm for Protecting Aggregate Business Microdata via a Remote System

Abstract

Releasing business microdata is a challenging problem for many statistical agencies. Businesses with distinct continuous characteristics such as extremely high income could easily be identified while these businesses are normally included in surveys representing the population. In order to provide data users with useful statistics while maintaining confidentiality, some statistical agencies have developed online based tools to allow users to specify and request tables created from microdata. These tools only release perturbed cell values generated from automatic output perturbation algorithms in order to protect each underlying observation against various attacks, such as differencing attacks. An example of the perturbation algorithms has been proposed by Thompson et al. (2013). The algorithm focuses largely on reducing disclosure risks without addressing much on data utility. As a result, the algorithm has limitations, including a limited scope of applicable cells and uncontrolled utility loss. In this paper we introduce a new algorithm for generating perturbed cell values. As a comparison, The new algorithm allows more control over utility loss, while it could also achieve better utility-disclosure tradeoffs in many cases, and is conjectured to be applicable to a wider scope of cells. Abstract. Releasing business microdata is a challenging problem for many statistical agencies. Businesses with distinct continuous characteristics such as extremely high income could easily be identified while these businesses are normally included in surveys representing the population. In order to provide data users with useful statistics while maintaining confidentiality , some statistical agencies have developed online based tools to allow users to specify and request tables created from microdata. These tools only release perturbed cell values generated from automatic output perturbation algorithms in order to protect each underlying observation against various attacks, such as differencing attacks. An example of the perturbation algorithms has been proposed by Thompson et al. (2013). The algorithm focuses largely on reducing disclosure risks without addressing much on data utility. As a result, the algorithm has limitations, including a limited scope of applicable cells and uncontrolled utility loss. In this paper we introduce a new algorithm for generating perturbed cell values. As a comparison, The new algorithm allows more control over utility loss, while it could also achieve better utility-disclosure tradeoffs in many cases, and is conjectured to be applicable to a wider scope of cells.

Investigating Attribute Risks and Constructing Linkage Error Models for Probabilistically-Linked Data

“…Attribute risks from differencing attacks can be reduced if an output perturbation algorithm is used. In the chapter, we introduce an innovative output perturbation algorithm against differencing attack strategies (see also [5]). The performance of the algorithm is compared with an algorithm in [8].…”

The Vulnerability of Multiplicative Noise Protection to Correlation-Attacks on Continuous Microdata

“…However, as noted in Nayak et al (2011), evaluating value disclosure risk is difficult because different data intruders may have different target values as well as different prior knowledge about the original data. To understand potential disclosure risks of a data masking mechanism, a common approach in the literature is to model intrusion behaviors ( Ma et al 2016). Correspondingly, appropriate actions could be made during data masking stage such that the released data is protected against these behaviours.…”