Statistical Disclosure Limitation: New Directions and Challenges

Main Article Content

Natalie Shlomo
https://orcid.org/0000-0003-0701-5080

Abstract

An overview of traditional types of data dissemination at statistical agencies is provided including definitions of disclosure risks, the quantification of disclosure risk and data utility and common statistical disclosure limitation (SDL) methods. However, with technological advancements and the increasing push by governments for open
and accessible data, new forms of data dissemination are currently being explored. We focus on web-based applications such as flexible table builders and remote analysis servers, synthetic data and remote access. Many of these applications introduce new challenges for statistical agencies as they are gradually relinquishing some of their control on what data is released. There is now more recognition of the need for perturbative methods to protect the confidentiality of data subjects. These new forms of data dissemination are changing the landscape of how disclosure risks are conceptualized and the types of SDL methods that need to be applied to protect the
data. In particular, inferential disclosure is the main disclosure risk of concern and encompasses the traditional types of disclosure risks based on identity and attribute disclosures. These challenges have led to statisticians exploring the computer science definition of differential privacy and privacy- by-design applications. We explore how differential privacy can be a useful addition to the current SDL framework within statistical agencies.

Article Details

How to Cite
Shlomo, Natalie. 2018. “Statistical Disclosure Limitation: New Directions and Challenges”. Journal of Privacy and Confidentiality 8 (1). https://doi.org/10.29012/jpc.684.
Section
Articles