Practical, Private Assurance of the Value of Collaboration via Fully Homomorphic Encryption

• URL: http://arxiv.org/abs/2310.02563v3
• Date: Mon, 07 Oct 2024 23:44:05 GMT
• Title: Practical, Private Assurance of the Value of Collaboration via Fully Homomorphic Encryption
• Authors: Hassan Jameel Asghar, Zhigang Lu, Zhongrui Zhao, Dali Kaafar,
• Abstract summary: Two parties wish to collaborate on their datasets. One party is promised an improvement on its prediction model by incorporating data from the other party. The parties would only wish to collaborate further if the updated model shows an improvement in accuracy.
• Score: 3.929854470352013
• License:
• Abstract: Two parties wish to collaborate on their datasets. However, before they reveal their datasets to each other, the parties want to have the guarantee that the collaboration would be fruitful. We look at this problem from the point of view of machine learning, where one party is promised an improvement on its prediction model by incorporating data from the other party. The parties would only wish to collaborate further if the updated model shows an improvement in accuracy. Before this is ascertained, the two parties would not want to disclose their models and datasets. In this work, we construct an interactive protocol for this problem based on the fully homomorphic encryption scheme over the Torus (TFHE) and label differential privacy, where the underlying machine learning model is a neural network. Label differential privacy is used to ensure that computations are not done entirely in the encrypted domain, which is a significant bottleneck for neural network training according to the current state-of-the-art FHE implementations. We formally prove the security of our scheme assuming honest-but-curious parties, but where one party may not have any expertise in labelling its initial dataset. Experiments show that we can obtain the output, i.e., the accuracy of the updated model, with time many orders of magnitude faster than a protocol using entirely FHE operations.

Related papers

• Federated Face Forgery Detection Learning with Personalized Representation [63.90408023506508]
  Deep generator technology can produce high-quality fake videos that are indistinguishable, posing a serious social threat. Traditional forgery detection methods directly centralized training on data. The paper proposes a novel federated face forgery detection learning with personalized representation.
  arXiv  Detail & Related papers  (2024-06-17T02:20:30Z)
• Incentives in Private Collaborative Machine Learning [56.84263918489519]
  Collaborative machine learning involves training models on data from multiple parties. We introduce differential privacy (DP) as an incentive. We empirically demonstrate the effectiveness and practicality of our approach on synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2024-04-02T06:28:22Z)
• When approximate design for fast homomorphic computation provides differential privacy guarantees [0.08399688944263842]
  Differential privacy (DP) and cryptographic primitives are popular countermeasures against privacy attacks. In this paper, we design SHIELD, a probabilistic approximation algorithm for the argmax operator. Even if SHIELD could have other applications, we here focus on one setting and seamlessly integrate it in the SPEED collaborative training framework.
  arXiv  Detail & Related papers  (2023-04-06T09:38:01Z)
• PEOPL: Characterizing Privately Encoded Open Datasets with Public Labels [59.66777287810985]
  We introduce information-theoretic scores for privacy and utility, which quantify the average performance of an unfaithful user. We then theoretically characterize primitives in building families of encoding schemes that motivate the use of random deep neural networks.
  arXiv  Detail & Related papers  (2023-03-31T18:03:53Z)
• Differentially Private Federated Learning on Heterogeneous Data [10.431137628048356]
  Federated Learning (FL) is a paradigm for large-scale distributed learning. It faces two key challenges: (i) efficient training from highly heterogeneous user data, and (ii) protecting the privacy of participating users. We propose a novel FL approach to tackle these two challenges together by incorporating Differential Privacy (DP) constraints.
  arXiv  Detail & Related papers  (2021-11-17T18:23:49Z)
• Collaborative Unsupervised Visual Representation Learning from Decentralized Data [34.06624704343615]
  We propose a novel federated unsupervised learning framework, FedU. In this framework, each party trains models from unlabeled data independently using contrastive learning with an online network and a target network. FedU preserves data privacy as each party only has access to its raw data.
  arXiv  Detail & Related papers  (2021-08-14T08:34:11Z)
• Representative & Fair Synthetic Data [68.8204255655161]
  We present a framework to incorporate fairness constraints into the self-supervised learning process. We generate a representative as well as fair version of the UCI Adult census data set. We consider representative & fair synthetic data a promising future building block to teach algorithms not on historic worlds, but rather on the worlds that we strive to live in.
  arXiv  Detail & Related papers  (2021-04-07T09:19:46Z)
• Decentralized Federated Learning Preserves Model and Data Privacy [77.454688257702]
  We propose a fully decentralized approach, which allows to share knowledge between trained models. Students are trained on the output of their teachers via synthetically generated input data. The results show that an untrained student model, trained on the teachers output reaches comparable F1-scores as the teacher.
  arXiv  Detail & Related papers  (2021-02-01T14:38:54Z)
• Additively Homomorphical Encryption based Deep Neural Network for Asymmetrically Collaborative Machine Learning [12.689643742151516]
  preserving machine learning creates a constraint which limits further applications in finance sectors. We propose a new practical scheme of collaborative machine learning that one party owns data, but another party owns labels only. Our experiments on different datasets demonstrate not only stable training without accuracy, but also more than 100 times speedup.
  arXiv  Detail & Related papers  (2020-07-14T06:43:25Z)
• Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing [55.012801269326594]
  In Byzantine robust distributed learning, a central server wants to train a machine learning model over data distributed across multiple workers. A fraction of these workers may deviate from the prescribed algorithm and send arbitrary messages. We propose a simple bucketing scheme that adapts existing robust algorithms to heterogeneous datasets at a negligible computational cost.
  arXiv  Detail & Related papers  (2020-06-16T17:58:53Z)
• Privacy-Preserving Gaussian Process Regression -- A Modular Approach to the Application of Homomorphic Encryption [4.1499725848998965]
  Homomorphic encryption (FHE) allows data to be computed on whilst encrypted. Some commonly used machine learning algorithms, such as Gaussian process regression, are poorly suited to FHE. We show that a modular approach, which applies FHE to only the sensitive steps of a workflow that need protection, allows one party to make predictions on their data.
  arXiv  Detail & Related papers  (2020-01-28T11:50:36Z)

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.