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Big data, differential privacy, and national statistical organisations — arXiv:2609.02495

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.02495
License (as archived): CC0-1.0
Archived: 2026-10-17T04:26:22.552Z
SHA-256 of the archived text: acfec1fb271c7b208699ac82e07f2960cec762b4a8438c616514e103b5c058f3
Stable URL of this page: https://metraipolis.com/documento/dc_907fb2f45bbd

How to cite

Big data, differential privacy, and national statistical organisations — arXiv:2609.02495 — arXiv (en), https://arxiv.org/abs/2609.02495 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_907fb2f45bbd (Metraipolis archive dc_907fb2f45bbd, sha256 acfec1fb271c7b208699ac82e07f2960cec762b4a8438c616514e103b5c058f3)

BibTeX

@misc{dc907fb2f45bbd,
  title = {Big data, differential privacy, and national statistical organisations — arXiv:2609.02495},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.02495},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_907fb2f45bbd; sha256 acfec1fb271c7b208699ac82e07f2960cec762b4a8438c616514e103b5c058f3},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.02495. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Differential privacy (DP) has emerged in the computer science literature as a measure of the impact on an individual's privacy resulting from the publication of a statistical output such as a frequency table. This paper provides an introduction to DP for official statisticians and discuss its relevance, benefits, and challenges from a National Statistical Organisation (NSO) perspective. We motivate our study by examining how privacy is evolving in the era of big data and how this might prompt a shift from traditional statistical disclosure techniques used in official statistics--which are generally applied on a cell-by-cell or table-by-table basis--to formal privacy methods, like DP, which are applied from a perspective encompassing the totality of the outputs generated from a given dataset. We identify an important interplay between DP's holistic privacy risk measure and the difficulty for NSOs in implementing DP, showing that DP's major advantage is also DP's major challenge. This paper provides new work addressing two key DP research areas for NSOs: DP's application to survey data and its incorporation within the Five Safes framework.

Tema degli esploratori: governance digitale e beni comuni (retro-catalogo arXiv: annunci del 2026-09-03 in cs.CY).
Categorie arXiv: Applications (stat.AP) ; Computers and Society (cs.CY).
Pagina del lavoro: https://arxiv.org/abs/2609.02495 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-17T04:04:04.801Z dalla pagina https://arxiv.org/catchup/cs.CY/2026-09-03?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).