Local Dampening: Differential Privacy for Non-numeric Queries via Local Sensitivity - de Farias, Victor Aguiar Evangelista; Machado, Javam de Castro (Anais Estendidos do Simpósio Brasileiro de Banco de Dados (SBBD), 2023)
Differential privacy is the state-of-the-art formal definition for data release under strong privacy guarantees. We present the local dampening mechanism a differentially private mechanism for non-numeric queries. Our approach is the first to leverage the notion of local sensitivity to reduce noise injected to the output. We develop a theoretical accuracy analysis to show the conditions that our approach performs accurately and we conduct an experimental evaluation with competitors on diverse problems. Those contributions were published on VDLB conference and on the special issue of the VLDB journal. Non-related contributions were published in SBBD, SBRC, CLOSER and FGCS. This work was carried out in cooperation with AT&T Labs Research - USA.