How Do Input Attributes Impact the Privacy Loss in Differential Privacy?

• URL: http://arxiv.org/abs/2211.10173v1
• Date: Fri, 18 Nov 2022 11:39:03 GMT
• Title: How Do Input Attributes Impact the Privacy Loss in Differential Privacy?
• Authors: Tamara T. Mueller, Stefan Kolek, Friederike Jungmann, Alexander Ziller, Dmitrii Usynin, Moritz Knolle, Daniel Rueckert and Georgios Kaissis
• Abstract summary: We study the connection between the per-subject norm in DP neural networks and individual privacy loss. We introduce a novel metric termed the Privacy Loss-Input Susceptibility (PLIS) which allows one to apportion the subject's privacy loss to their input attributes.
• Score: 55.492422758737575
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Differential privacy (DP) is typically formulated as a worst-case privacy guarantee over all individuals in a database. More recently, extensions to individual subjects or their attributes, have been introduced. Under the individual/per-instance DP interpretation, we study the connection between the per-subject gradient norm in DP neural networks and individual privacy loss and introduce a novel metric termed the Privacy Loss-Input Susceptibility (PLIS), which allows one to apportion the subject's privacy loss to their input attributes. We experimentally show how this enables the identification of sensitive attributes and of subjects at high risk of data reconstruction.

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