Local Dampening: Differential Privacy for Non-numeric Queries via Local Sensitivity

  • Victor Aguiar Evangelista de Farias Universidade Federal do Ceará (UFC)
  • Javam de Castro Machado Universidade Federal do Ceará (UFC)


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.

Palavras-chave: Differential privacy, Data anonymization, Graph analysis, Decision Trees


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DE FARIAS, Victor Aguiar Evangelista; MACHADO, Javam de Castro. Local Dampening: Differential Privacy for Non-numeric Queries via Local Sensitivity. In: CONCURSO DE TESES E DISSERTAÇÕES (CTDBD) - SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 38. , 2023, Belo Horizonte/MG. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 285-299. DOI: https://doi.org/10.5753/sbbd_estendido.2023.232504.