Privacy-Preserving Recommender Systems with Synthetic Query Generation
  using Differentially Private Large Language Models
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.
645c49717d655680b57d64e9
Aldo Gael Carranza
645c49717d655680b57d64ea
Rezsa Farahani
645c49717d655680b57d64eb
Natalia Ponomareva
645c49717d655680b57d64ec
Alex Kurakin
645c49717d655680b57d64ed
Matthew Jagielski
extracted_confirmed
2023-11-07T14:32:02.254Z
654a4a3b6a46bad8c0f2ab80
/avatars/08caf74525ea3e46fe58c20eb1f3d64d.svg
Matthew Jagielski
user
jagielski
645c49717d655680b57d64ee
Milad Nasr
extracted_pending
2023-05-11T01:48:33.565Z
630ea3eba97116192fada929
/avatars/bf37a22e234d8c3a7da53f8853f49502.svg
Milad
user
srxzr
Privacy-Preserving Recommender Systems with Synthetic Query Generation
  using Differentially Private Large Language Models
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.
A method using differentially private large language models for generating private synthetic queries enhances the security and effectiveness of deep retrieval models in recommender systems.
differentially private (DP) large language models (LLMs) DP training fine-tuning synthetic queries deep retrieval models query-level privacy guarantees