FastLloyd: Federated, Accurate, Secure, and Tunable $k$-Means Clustering with Differential Privacy

• URL: http://arxiv.org/abs/2405.02437v1
• Date: Fri, 3 May 2024 19:04:37 GMT
• Title: FastLloyd: Federated, Accurate, Secure, and Tunable $k$-Means Clustering with Differential Privacy
• Authors: Abdulrahman Diaa, Thomas Humphries, Florian Kerschbaum,
• Abstract summary: We study the problem of privacy-preserving $k$-means clustering in the horizontally federated setting. Existing approaches using secure computation suffer from substantial overheads and do not offer output privacy. By utilizing the computational DP model, we design a lightweight, secure aggregation-based approach that achieves four orders of magnitude speed-up.
• Score: 26.927356987142407
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We study the problem of privacy-preserving $k$-means clustering in the horizontally federated setting. Existing federated approaches using secure computation, suffer from substantial overheads and do not offer output privacy. At the same time, differentially private (DP) $k$-means algorithms assume a trusted central curator and do not extend to federated settings. Naively combining the secure and DP solutions results in a protocol with impractical overhead. Instead, our work provides enhancements to both the DP and secure computation components, resulting in a design that is faster, more private, and more accurate than previous work. By utilizing the computational DP model, we design a lightweight, secure aggregation-based approach that achieves four orders of magnitude speed-up over state-of-the-art related work. Furthermore, we not only maintain the utility of the state-of-the-art in the central model of DP, but we improve the utility further by taking advantage of constrained clustering techniques.

Related papers

• Secure Stateful Aggregation: A Practical Protocol with Applications in Differentially-Private Federated Learning [36.42916779389165]
  DP-FTRL based approaches have already seen widespread deployment in industry. We introduce secure stateful aggregation: a simple append-only data structure that allows for the private storage of aggregate values. We observe that secure stateful aggregation suffices for realizing DP-FTRL-based private federated learning.
  arXiv  Detail & Related papers  (2024-10-15T07:45:18Z)
• Private and Federated Stochastic Convex Optimization: Efficient Strategies for Centralized Systems [8.419845742978985]
  This paper addresses the challenge of preserving privacy in Federated Learning (FL) within centralized systems. We devise methods that ensure Differential Privacy (DP) while maintaining optimal convergence rates for homogeneous and heterogeneous data distributions.
  arXiv  Detail & Related papers  (2024-07-17T08:19:58Z)
• Correlated Privacy Mechanisms for Differentially Private Distributed Mean Estimation [8.660393575612169]
  CorDP-DME is a novel DP-DME that spans the gap between local differential privacy (LDP) and distributed DP (SecAgg) We provide an information-theoretic analysis of CorDP-DME, and derive theoretical guarantees for utility under any given privacy parameters.
  arXiv  Detail & Related papers  (2024-07-03T17:22:33Z)
• Improved Communication-Privacy Trade-offs in $L_2$ Mean Estimation under Streaming Differential Privacy [47.997934291881414]
  Existing mean estimation schemes are usually optimized for $L_infty$ geometry and rely on random rotation or Kashin's representation to adapt to $L$ geometry. We introduce a novel privacy accounting method for the sparsified Gaussian mechanism that incorporates the randomness inherent in sparsification into the DP. Unlike previous approaches, our accounting algorithm directly operates in $L$ geometry, yielding MSEs that fast converge to those of the Gaussian mechanism.
  arXiv  Detail & Related papers  (2024-05-02T03:48:47Z)
• Provable Privacy with Non-Private Pre-Processing [56.770023668379615]
  We propose a general framework to evaluate the additional privacy cost incurred by non-private data-dependent pre-processing algorithms. Our framework establishes upper bounds on the overall privacy guarantees by utilising two new technical notions.
  arXiv  Detail & Related papers  (2024-03-19T17:54:49Z)
• 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 Stochastic Optimization with Inherent Privacy Protection [103.62463469366557]
  Decentralized optimization is the basic building block of modern collaborative machine learning, distributed estimation and control, and large-scale sensing. Since involved data, privacy protection has become an increasingly pressing need in the implementation of decentralized optimization algorithms.
  arXiv  Detail & Related papers  (2022-05-08T14:38:23Z)
• Differentially Private Federated Bayesian Optimization with Distributed Exploration [48.9049546219643]
  We introduce differential privacy (DP) into the training of deep neural networks through a general framework for adding DP to iterative algorithms. We show that DP-FTS-DE achieves high utility (competitive performance) with a strong privacy guarantee. We also use real-world experiments to show that DP-FTS-DE induces a trade-off between privacy and utility.
  arXiv  Detail & Related papers  (2021-10-27T04:11:06Z)
• Federated Learning with Sparsification-Amplified Privacy and Adaptive Optimization [27.243322019117144]
  Federated learning (FL) enables distributed agents to collaboratively learn a centralized model without sharing their raw data with each other. We propose a new FL framework with sparsification-amplified privacy. Our approach integrates random sparsification with gradient perturbation on each agent to amplify privacy guarantee.
  arXiv  Detail & Related papers  (2020-08-01T20:22:57Z)
• User-Level Privacy-Preserving Federated Learning: Analysis and Performance Optimization [77.43075255745389]
  Federated learning (FL) is capable of preserving private data from mobile terminals (MTs) while training the data into useful models. From a viewpoint of information theory, it is still possible for a curious server to infer private information from the shared models uploaded by MTs. We propose a user-level differential privacy (UDP) algorithm by adding artificial noise to the shared models before uploading them to servers.
  arXiv  Detail & Related papers  (2020-02-29T10:13:39Z)

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.