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CAT-LDP: Cloud-edge Adaptive Taxonomy under Local Differential Privacy — arXiv:2609.05095

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.05095
License (as archived): CC0-1.0
Archived: 2026-10-16T23:52:03.527Z
SHA-256 of the archived text: adaa3383bc7e193a1c54525862ffc1b40d2f752fcb34749f2043228b4cc4e577
Stable URL of this page: https://metraipolis.com/documento/dc_3205c15522c8

How to cite

CAT-LDP: Cloud-edge Adaptive Taxonomy under Local Differential Privacy — arXiv:2609.05095 — arXiv (en), https://arxiv.org/abs/2609.05095 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_3205c15522c8 (Metraipolis archive dc_3205c15522c8, sha256 adaa3383bc7e193a1c54525862ffc1b40d2f752fcb34749f2043228b4cc4e577)

BibTeX

@misc{dc3205c15522c8,
  title = {CAT-LDP: Cloud-edge Adaptive Taxonomy under Local Differential Privacy — arXiv:2609.05095},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.05095},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_3205c15522c8; sha256 adaa3383bc7e193a1c54525862ffc1b40d2f752fcb34749f2043228b4cc4e577},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.05095. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Recommender systems are widely used in daily life, but their direct collection and use of user preference data can also lead to privacy leakage. Existing privacy-preserving recommendation methods often find it hard to balance user privacy and recommendation performance. This problem is more serious in implicit-feedback settings, where data sparsity further increases the loss of useful signals caused by privacy perturbation. To solve this problem, we propose CAT-LDP, a cloud-local collaborative recommendation framework under local differential privacy constraints. CAT-LDP combines a hierarchical taxonomy tree with an adaptive privacy budget allocation strategy to keep more useful signals in users' active categories while protecting user privacy. Specifically, users upload perturbed category profiles that satisfy LDP. Based on these profiles, the cloud performs coarse-grained candidate generation, and the local device then carries out fine-grained reranking by using unperturbed local history. Experiments on the Amazon Video Games dataset show that CAT-LDP consistently outperforms its fixed-budget ablation variant and representative baselines on HR@K and NDCG@K under different privacy budgets. The results show that combining category-space modeling with cloud-local task decoupling can effectively reduce noise amplification in long-tail sparse settings and provide a better balance between privacy and utility for implicit-feedback recommendation.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-07 in cs.DB).
Categorie arXiv: Databases (cs.DB).
Pagina del lavoro: https://arxiv.org/abs/2609.05095 — 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 2026-10-16T23:21:52.150Z dalla pagina https://arxiv.org/catchup/cs.DB/2026-09-07?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).