a,m-warden: a syntactic privacy notion for continuous data publishing against probabilistic attacks
Tobar Nicolau, Adrián, Javier Parra Arnau, Jordi Forné · UPCommons institutional repository (Universitat Politècnica de Catalunya) · 2022
Publishing private information can be very useful for multiple data mining processes, but prior anonymization is necessary to protect the privacy of the individuals on whom data are collected. This is especially sensitive in dynamic publishing scenarios where multiple releases with common information are published over time; and even more so against probabilistic attackers, i.e., those with statistical knowledge of the dataset. In this paper, we focus on the specific scenario of continuous data publishing, where existing privacy notions cannot offer protection against those attackers in dynamic settings (i.e., where record additions and deletions are considered). To tackle this limitation, we propose a,mwarden, a new syntactic privacy notion that can provide an explicit upper bound on disclosure risk against such attacks under the Smart Random World Assumption. We demonstrate the theoretical guarantees of this notion, prove the complexity of an optimal implementation, propose a proof-of-concept implementation of an algorithm that can enforce it, and perform an experimental evaluation to support our claims. Our experimental results show that a,m-warden can reduce the performance and certainty of probabilistic attacks by more than 10% with respect to tsafety.