Practical and Secure Federated Recommendation with Personalized Masks

• URL: http://arxiv.org/abs/2109.02464v1
• Date: Wed, 18 Aug 2021 07:12:23 GMT
• Title: Practical and Secure Federated Recommendation with Personalized Masks
• Authors: Liu Yang, Ben Tan, Bo Liu, Vincent W. Zheng, Kai Chen, Qiang Yang
• Abstract summary: Federated recommendation is a new notion of private distributed recommender systems. Current recommender systems mainly utilize homomorphic encryption and differential privacy methods. In this paper, we propose a new federated recommendation framework, named federated masked matrix factorization.
• Score: 24.565751694946062
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated recommendation is a new notion of private distributed recommender systems. It aims to address the data silo and privacy problems altogether. Current federated recommender systems mainly utilize homomorphic encryption and differential privacy methods to protect the intermediate computational results. However, the former comes with extra communication and computation costs, the latter damages model accuracy. Neither of them could simultaneously satisfy the real-time feedback and accurate personalization requirements of recommender systems. In this paper, we proposed a new federated recommendation framework, named federated masked matrix factorization. Federated masked matrix factorization could protect the data privacy in federated recommender systems without sacrificing efficiency or efficacy. Instead of using homomorphic encryption and differential privacy, we utilize the secret sharing technique to incorporate the secure aggregation process of federated matrix factorization. Compared with homomorphic encryption, secret sharing largely speeds up the whole training process. In addition, we introduce a new idea of personalized masks and apply it in the proposed federated masked matrix factorization framework. On the one hand, personalized masks could further improve efficiency. On the other hand, personalized masks also benefit efficacy. Empirically, we show the superiority of the designed model on different real-world data sets. Besides, we also provide the privacy guarantee and discuss the extension of the personalized mask method to the general federated learning tasks.

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)
• Masked Differential Privacy [64.32494202656801]
  We propose an effective approach called masked differential privacy (DP), which allows for controlling sensitive regions where differential privacy is applied. Our method operates selectively on data and allows for defining non-sensitive-temporal regions without DP application or combining differential privacy with other privacy techniques within data samples.
  arXiv  Detail & Related papers  (2024-10-22T15:22:53Z)
• Personalized Federated Collaborative Filtering: A Variational AutoEncoder Approach [49.63614966954833]
  Federated Collaborative Filtering (FedCF) is an emerging field focused on developing a new recommendation framework with preserving privacy. This paper proposes a novel personalized FedCF method by preserving users' personalized information into a latent variable and a neural model simultaneously. To effectively train the proposed framework, we model the problem as a specialized Variational AutoEncoder (VAE) task by integrating user interaction vector reconstruction with missing value prediction.
  arXiv  Detail & Related papers  (2024-08-16T05:49:14Z)
• Adaptive Hybrid Masking Strategy for Privacy-Preserving Face Recognition Against Model Inversion Attack [7.82336679905826]
  This paper introduces an adaptive hybrid masking algorithm against model inversion attacks (MIA) Specifically, face images are masked in the frequency domain using an adaptive MixUp strategy. Experimental results demonstrate that our proposed hybrid masking scheme outperforms existing defense algorithms in terms of privacy preservation and recognition accuracy against MIA.
  arXiv  Detail & Related papers  (2024-03-14T02:17:57Z)
• A Learning-based Declarative Privacy-Preserving Framework for Federated Data Management [23.847568516724937]
  We introduce a new privacy-preserving technique that uses a deep learning model trained using Differentially-Private Descent (DP-SGD) algorithm. We then demonstrate a novel declarative privacy-preserving workflow that allows users to specify "what private information to protect" rather than "how to protect"
  arXiv  Detail & Related papers  (2024-01-22T22:50:59Z)
• Diff-Privacy: Diffusion-based Face Privacy Protection [58.1021066224765]
  In this paper, we propose a novel face privacy protection method based on diffusion models, dubbed Diff-Privacy. Specifically, we train our proposed multi-scale image inversion module (MSI) to obtain a set of SDM format conditional embeddings of the original image. Based on the conditional embeddings, we design corresponding embedding scheduling strategies and construct different energy functions during the denoising process to achieve anonymization and visual identity information hiding.
  arXiv  Detail & Related papers  (2023-09-11T09:26:07Z)
• Theoretically Principled Federated Learning for Balancing Privacy and Utility [61.03993520243198]
  We propose a general learning framework for the protection mechanisms that protects privacy via distorting model parameters. It can achieve personalized utility-privacy trade-off for each model parameter, on each client, at each communication round in federated learning.
  arXiv  Detail & Related papers  (2023-05-24T13:44:02Z)
• Decentralized Matrix Factorization with Heterogeneous Differential Privacy [2.4743508801114444]
  We propose a novel Heterogeneous Differentially Private Matrix Factorization algorithm (denoted as HDPMF) for untrusted recommender. Our framework uses modified stretching mechanism with an innovative rescaling scheme to achieve better trade off between privacy and accuracy.
  arXiv  Detail & Related papers  (2022-12-01T06:48:18Z)
• PrivMVMF: Privacy-Preserving Multi-View Matrix Factorization for Recommender Systems [0.190365714903665]
  We propose a new privacy-preserving framework based on homomorphic encryption, Privacy-Preserving Multi-View Matrix Factorization (PrivMVMF) PrivMVMF is successfully implemented and tested thoroughly with the MovieLens dataset.
  arXiv  Detail & Related papers  (2022-09-29T03:21:24Z)
• OPOM: Customized Invisible Cloak towards Face Privacy Protection [58.07786010689529]
  We investigate the face privacy protection from a technology standpoint based on a new type of customized cloak. We propose a new method, named one person one mask (OPOM), to generate person-specific (class-wise) universal masks. The effectiveness of the proposed method is evaluated on both common and celebrity datasets.
  arXiv  Detail & Related papers  (2022-05-24T11:29:37Z)
• Privacy Threats Against Federated Matrix Factorization [14.876668437269817]
  We study the privacy threats of the matrix factorization method in the federated learning framework. This is the first study of privacy threats of the matrix factorization method in the federated learning framework.
  arXiv  Detail & Related papers  (2020-07-03T09:58:52Z)

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.