Statistical Disclosure Limitation: New Directions and Challenges

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Abstract

An overview of traditional types of data dissemination at statistical agenciesis provided including definitions of disclosure risks, the quantification of disclosure risk anddata utility and common statistical disclosure limitation (SDL) methods. However, withtechnological advancements and the increasing push by governments for open and accessibledata, new forms of data dissemination are currently being explored. We focus on web-basedapplications such as flexible table builders and remote analysis servers, synthetic data andremote access. Many of these applications introduce new challenges for statistical agenciesas they are gradually relinquishing some of their control on what data is released. There isnow more recognition of the need for perturbative methods to protect the confidentialityof data subjects. These new forms of data dissemination are changing the landscape ofhow disclosure risks are conceptualized and the types of SDL methods that need to beapplied to protect the data. In particular, inferential disclosure is the main disclosure riskof concern and encompasses the traditional types of disclosure risks based on identity andattribute disclosures. These challenges have led to statisticians exploring the computerscience definition of differential privacy and privacy- by-design applications. We explorehow differential privacy can be a useful addition to the current SDL framework withinstatistical agencies.Key words and phrases:Inferential Disclosure, Disclosure Risk, Data Utility, Differential Privacy.www.journalprivacyconfidentiality.orgDOI:10.29012/jpc.684c©N. ShlomoCreative Commons (CC BY-NC-ND 4.0)
Original languageEnglish
JournalJournal of Privacy and Confidentiality
Volume8
Issue number1
DOIs
Publication statusPublished - 24 Dec 2018

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