Risk-Utility Paradigms for Statistical Disclosure Limitation: How to Think, But Not How to Act

Self Cite

We compare two general strategies for performing statistical disclosure limitation (SDL) for continuous microdata subject to edit rules. In the first, existing SDL methods are applied, and any constraint-violating values they produce are replaced using a constraint-preserving imputation procedure. In the second, the SDL methods are modified to prevent them from generating violations. We present a simulation study, based on data from the Colombian Annual Manufacturing Survey, that evaluates the performance of the two strategies as applied to several SDL methods. The results suggest that differences in risk-utility profiles across SDL methods dwarf differences between the two general strategies. Among the SDL strategies, variants of microaggregation and partially synthetic data offer the most attractive risk-utility profiles.

Section: Discussionmentioning

confidence: 99%

Section: Simulation Studymentioning

confidence: 99%

Statistical Disclosure Limitation in the Presence of Edit Rules

Kim¹,

Karr

Reiter

2015

Self Cite

“…Therein lies the challenge: not restricting the former while thwarting the latter. This challenge is discussed in detail by Cox et al (2011).…”

Section: Protection: Imposing General Restrictionsmentioning

confidence: 99%

Disclosure-Protected Inference with Linked Microdata Using a Remote Analysis Server

Chipperfield¹

2014

Large amounts of microdata are collected by data custodians in the form of censuses and administrative records. Often, data custodians will collect different information on the same individual. Many important questions can be answered by linking microdata collected by different data custodians. For this reason, there is very strong demand from analysts, within government, business, and universities, for linked microdata. However, many data custodians are legally obliged to ensure the risk of disclosing information about a person or organisation is acceptably low. Different authors have considered the problem of how to facilitate reliable statistical inference from analysis of linked microdata while ensuring that the risk of disclosure is acceptably low. This article considers the problem from the perspective of an Integrating Authority that, by definition, is trusted to link the microdata and to facilitate analysts' access to the linked microdata via a remote server, which allows analysts to fit models and view the statistical output without being able to observe the underlying linked microdata. One disclosure risk that must be managed by an Integrating Authority is that one data custodian may use the microdata it supplied to the Integrating Authority and statistical output released from the remote server to disclose information about a person or organisation that was supplied by the other data custodian. This article considers analysis of only binary variables. The utility and disclosure risk of the proposed method are investigated both in a simulation and using a real example. This article shows that some popular protections against disclosure (dropping records, rounding regression coefficients or imposing restrictions on model selection) can be ineffective in the above setting.

“…In a related setting (Cox et al 2011) I and co-authors have criticized risk-utility paradigms in statistical disclosure limitation as useful for "how to think, but not how to act." By this, we distinguish frameworks for structuring decisions from systems and architectures for making decisions.…”

Section: The Five Articles In the Context Of The Eltinge/biemer/holmbmentioning

confidence: 99%

Discussion

Karr¹

2013

Self Cite

These five articles and the framework proposed by Eltinge, Biemer, and Holmberg illuminate an exciting view of the future of official statistics. The articles are innovative, forward-looking, and remarkably complementary to one another. Below, for brevity, I refer to the five articles as the NASS, StatsNL, StatsNZ, RTI and Westat/NCHS articles. I will comment on each article individually, on them collectively in the context of the framework, and on the entire concept of “systems and architectures for high-quality statistics production.” First, however, I present two complementary perspectives on official statistics.