TY - JOUR AU - Shlomo, Natalie PY - 2018/12/24 Y2 - 2024/03/29 TI - Statistical Disclosure Limitation: New Directions and Challenges JF - Journal of Privacy and Confidentiality JA - JPC VL - 8 IS - 1 SE - Articles DO - 10.29012/jpc.684 UR - https://journalprivacyconfidentiality.org/index.php/jpc/article/view/684 SP - AB - <p>An overview of traditional types of data dissemination at statistical agencies is&nbsp;provided including definitions of disclosure risks, the quantification of disclosure risk&nbsp;and data utility and common statistical disclosure limitation (SDL) methods. However,&nbsp;with technological advancements and the increasing push by governments for open<br>and accessible data, new forms of data dissemination are currently being explored. We&nbsp;focus on web-based applications such as flexible table builders and remote analysis&nbsp;servers, synthetic data and remote access. Many of these applications introduce new&nbsp;challenges for statistical agencies as they are gradually relinquishing some of their&nbsp;control on what data is released. There is now more recognition of the need for&nbsp;perturbative methods to protect the confidentiality of data subjects. These new forms&nbsp;of data dissemination are changing the landscape of how disclosure risks are&nbsp;conceptualized and the types of SDL methods that need to be applied to protect the<br>data. In particular, inferential disclosure is the main disclosure risk of concern and&nbsp;encompasses the traditional types of disclosure risks based on identity and attribute&nbsp;disclosures. These challenges have led to statisticians exploring the computer science&nbsp;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&nbsp;statistical agencies.</p> ER -