title: Privacy-Preserving Recommender Systems with Synthetic Query Generation
    using Differentially Private Large Language Models
summary: We propose a novel approach for developing privacy-preserving large-scale
  recommender systems using differentially private (DP) large language models
  (LLMs) which overcomes certain challenges and limitations in DP training these
  complex systems. Our method is particularly well suited for the emerging area
  of LLM-based recommender systems, but can be readily employed for any
  recommender systems that process representations of natural language inputs.
  Our approach involves using DP training methods to fine-tune a publicly
  pre-trained LLM on a query generation task. The resulting model can generate
  private synthetic queries representative of the original queries which can be
  freely shared for any downstream non-private recommendation training procedures
  without incurring any additional privacy cost. We evaluate our method on its
  ability to securely train effective deep retrieval models, and we observe
  significant improvements in their retrieval quality without compromising
  query-level privacy guarantees compared to methods where the retrieval models
  are directly DP trained.
paper_authors._id: 645c49717d655680b57d64e9
paper_authors.name: Aldo Gael Carranza
paper_authors._id: 645c49717d655680b57d64ea
paper_authors.name: Rezsa Farahani
paper_authors._id: 645c49717d655680b57d64eb
paper_authors.name: Natalia Ponomareva
paper_authors._id: 645c49717d655680b57d64ec
paper_authors.name: Alex Kurakin
paper_authors._id: 645c49717d655680b57d64ed
paper_authors.name: Matthew Jagielski
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2023-11-07T14:32:02.254Z
paper_authors._id: 654a4a3b6a46bad8c0f2ab80
paper_authors.avatarUrl: /avatars/08caf74525ea3e46fe58c20eb1f3d64d.svg
paper_authors.fullname: Matthew Jagielski
paper_authors.type: user
paper_authors.user: jagielski
paper_authors._id: 645c49717d655680b57d64ee
paper_authors.name: Milad Nasr
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2023-05-11T01:48:33.565Z
paper_authors._id: 630ea3eba97116192fada929
paper_authors.avatarUrl: /avatars/bf37a22e234d8c3a7da53f8853f49502.svg
paper_authors.fullname: Milad
paper_authors.type: user
paper_authors.user: srxzr
paper_title: Privacy-Preserving Recommender Systems with Synthetic Query Generation
    using Differentially Private Large Language Models
paper_summary: We propose a novel approach for developing privacy-preserving large-scale
  recommender systems using differentially private (DP) large language models
  (LLMs) which overcomes certain challenges and limitations in DP training these
  complex systems. Our method is particularly well suited for the emerging area
  of LLM-based recommender systems, but can be readily employed for any
  recommender systems that process representations of natural language inputs.
  Our approach involves using DP training methods to fine-tune a publicly
  pre-trained LLM on a query generation task. The resulting model can generate
  private synthetic queries representative of the original queries which can be
  freely shared for any downstream non-private recommendation training procedures
  without incurring any additional privacy cost. We evaluate our method on its
  ability to securely train effective deep retrieval models, and we observe
  significant improvements in their retrieval quality without compromising
  query-level privacy guarantees compared to methods where the retrieval models
  are directly DP trained.
paper_ai_keywords: differentially private (DP) large language models (LLMs) recommender systems fine-tune synthetic queries deep retrieval models privacy-preserving query-level privacy guarantees