User Consented Federated Recommender System Against Personalized Attribute Inference Attack

• URL: http://arxiv.org/abs/2312.16203v1
• Date: Sat, 23 Dec 2023 09:44:57 GMT
• Title: User Consented Federated Recommender System Against Personalized Attribute Inference Attack
• Authors: Qi Hu, Yangqiu Song
• Abstract summary: We propose a user-consented federated recommendation system (UC-FedRec) to flexibly satisfy the different privacy needs of users. UC-FedRec allows users to self-define their privacy preferences to meet various demands and makes recommendations with user consent.
• Score: 55.24441467292359
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recommender systems can be privacy-sensitive. To protect users' private historical interactions, federated learning has been proposed in distributed learning for user representations. Using federated recommender (FedRec) systems, users can train a shared recommendation model on local devices and prevent raw data transmissions and collections. However, the recommendation model learned by a common FedRec may still be vulnerable to private information leakage risks, particularly attribute inference attacks, which means that the attacker can easily infer users' personal attributes from the learned model. Additionally, traditional FedRecs seldom consider the diverse privacy preference of users, leading to difficulties in balancing the recommendation utility and privacy preservation. Consequently, FedRecs may suffer from unnecessary recommendation performance loss due to over-protection and private information leakage simultaneously. In this work, we propose a novel user-consented federated recommendation system (UC-FedRec) to flexibly satisfy the different privacy needs of users by paying a minimum recommendation accuracy price. UC-FedRec allows users to self-define their privacy preferences to meet various demands and makes recommendations with user consent. Experiments conducted on different real-world datasets demonstrate that our framework is more efficient and flexible compared to baselines.

Related papers

• Efficient and Robust Regularized Federated Recommendation [52.24782464815489]
  The recommender system (RSRS) addresses both user preference and privacy concerns. We propose a novel method that incorporates non-uniform gradient descent to improve communication efficiency. RFRecF's superior robustness compared to diverse baselines.
  arXiv  Detail & Related papers  (2024-11-03T12:10:20Z)
• FedRec+: Enhancing Privacy and Addressing Heterogeneity in Federated Recommendation Systems [15.463595798992621]
  FedRec+ is an ensemble framework for federated recommendation systems. It enhances privacy and reduces communication costs for edge users. Experimental results demonstrate the state-of-the-art performance of FedRec+.
  arXiv  Detail & Related papers  (2023-10-31T05:36:53Z)
• Privacy-Preserving Matrix Factorization for Recommendation Systems using Gaussian Mechanism [2.84279467589473]
  We propose a privacy-preserving recommendation system based on the differential privacy framework and matrix factorization. As differential privacy is a powerful and robust mathematical framework for designing privacy-preserving machine learning algorithms, it is possible to prevent adversaries from extracting sensitive user information.
  arXiv  Detail & Related papers  (2023-04-11T13:50:39Z)
• A Privacy Preserving System for Movie Recommendations Using Federated Learning [12.751432553199628]
  We present a recommender system for movie recommendations, which provides privacy and thus trustworthiness on multiple levels. It is trained using federated learning and thus, by its very nature, privacy-preserving. A novel federated learning scheme, called FedQ, is employed, which not only addresses the problem of non-i.i.d.-ness and small local datasets.
  arXiv  Detail & Related papers  (2023-03-07T17:22:38Z)
• Dual Personalization on Federated Recommendation [50.4115315992418]
  Federated recommendation is a new Internet service architecture that aims to provide privacy-preserving recommendation services in federated settings. This paper proposes a novel Personalized Federated Recommendation (PFedRec) framework to learn many user-specific lightweight models. We also propose a new dual personalization mechanism to effectively learn fine-grained personalization on both users and items.
  arXiv  Detail & Related papers  (2023-01-16T05:26:07Z)
• FedGRec: Federated Graph Recommender System with Lazy Update of Latent Embeddings [108.77460689459247]
  We propose a Federated Graph Recommender System (FedGRec) to mitigate privacy concerns. In our system, users and the server explicitly store latent embeddings for users and items, where the latent embeddings summarize different orders of indirect user-item interactions. We perform extensive empirical evaluations to verify the efficacy of using latent embeddings as a proxy of missing interaction graph.
  arXiv  Detail & Related papers  (2022-10-25T01:08:20Z)
• FedCL: Federated Contrastive Learning for Privacy-Preserving Recommendation [98.5705258907774]
  FedCL can exploit high-quality negative samples for effective model training with privacy well protected. We first infer user embeddings from local user data through the local model on each client, and then perturb them with local differential privacy (LDP) Since individual user embedding contains heavy noise due to LDP, we propose to cluster user embeddings on the server to mitigate the influence of noise.
  arXiv  Detail & Related papers  (2022-04-21T02:37:10Z)
• A Novel Privacy-Preserved Recommender System Framework based on Federated Learning [0.0]
  This paper proposed a novel privacy-preserved recommender system framework (PPRSF) The PPRSF not only able to reduces the privacy leakage risk, satisfies legal and regulatory requirements but also allows various recommendation algorithms to be applied.
  arXiv  Detail & Related papers  (2020-11-11T08:07:58Z)
• A Federated Multi-View Deep Learning Framework for Privacy-Preserving Recommendations [25.484225182093947]
  Privacy-preserving recommendations are gaining momentum due to concerns over user privacy and data security. FedRec algorithms have been proposed to realize personalized privacy-preserving recommendations. This paper presents FLMV-DSSM, a generic content-based federated multi-view recommendation framework.
  arXiv  Detail & Related papers  (2020-08-25T04:19:40Z)
• Reward Constrained Interactive Recommendation with Natural Language Feedback [158.8095688415973]
  We propose a novel constraint-augmented reinforcement learning (RL) framework to efficiently incorporate user preferences over time. Specifically, we leverage a discriminator to detect recommendations violating user historical preference. Our proposed framework is general and is further extended to the task of constrained text generation.
  arXiv  Detail & Related papers  (2020-05-04T16:23:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.