Multi-client Functional Encryption for Set Intersection with Non-monotonic Access Structures in Federated Learning

• URL: http://arxiv.org/abs/2412.09259v1
• Date: Thu, 12 Dec 2024 13:19:12 GMT
• Title: Multi-client Functional Encryption for Set Intersection with Non-monotonic Access Structures in Federated Learning
• Authors: Ruyuan Zhang, Jinguang Han,
• Abstract summary: Federated learning (FL) based on cloud servers is a distributed machine learning framework.<n>We propose a multi-client functional encryption scheme for set intersection with non-monotonic access structures.
• Score: 1.2891210250935148
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated learning (FL) based on cloud servers is a distributed machine learning framework that involves an aggregator and multiple clients, which allows multiple clients to collaborate in training a shared model without exchanging data. Considering the confidentiality of training data, several schemes employing functional encryption (FE) have been presented. However, existing schemes cannot express complex access control policies. In this paper, to realize more flexible and fine-grained access control, we propose a multi-client functional encryption scheme for set intersection with non-monotonic access structures (MCFE-SI-NAS), where multiple clients co-exist and encrypt independently without interaction. All ciphertexts are associated with an label, which can resist "mix-and-match" attacks. Aggregator can aggregate ciphertexts, but cannot know anything about the plaintexts. We first formalize the definition and security model for the MCFE-SI-NAS scheme and build a concrete construction based on asymmetric prime-order pairings. The security of our scheme is formally proven. Finally, we implement our MCFE-SI-NAS scheme and provide its efficiency analysis.

Related papers

• Fundamental Limits of Hierarchical Secure Aggregation with Cyclic User Association [93.46811590752814]
  Hierarchical secure aggregation is motivated by federated learning. In this paper, we consider HSA with a cyclic association pattern where each user is connected to $B$ consecutive relays. We propose an efficient aggregation scheme which includes a message design for the inputs inspired by gradient coding.
  arXiv  Detail & Related papers  (2025-03-06T15:53:37Z)
• Secure Aggregation in Federated Learning using Multiparty Homomorphic Encryption [17.98928470989766]
  A key operation in federated learning is the aggregation of gradient vectors generated by individual client nodes. We develop a method based on multiparty homomorphic encryption (MPHE) that enables the central node to compute this aggregate. We extend classical MPHE methods so that the decryption of the aggregate vector can be successful even when only a subset of client nodes are available.
  arXiv  Detail & Related papers  (2025-03-01T18:19:25Z)
• EncCluster: Scalable Functional Encryption in Federated Learning through Weight Clustering and Probabilistic Filters [3.9660142560142067]
  Federated Learning (FL) enables model training across decentralized devices by communicating solely local model updates to an aggregation server. FL remains vulnerable to inference attacks during model update transmissions. We present EncCluster, a novel method that integrates model compression through weight clustering with recent decentralized FE and privacy-enhancing data encoding.
  arXiv  Detail & Related papers  (2024-06-13T14:16:50Z)
• Decoupled Subgraph Federated Learning [57.588938805581044]
  We address the challenge of federated learning on graph-structured data distributed across multiple clients. We present a novel framework for this scenario, named FedStruct, that harnesses deep structural dependencies. We validate the effectiveness of FedStruct through experimental results conducted on six datasets for semi-supervised node classification.
  arXiv  Detail & Related papers  (2024-02-29T13:47:23Z)
• Coding-Based Hybrid Post-Quantum Cryptosystem for Non-Uniform Information [53.85237314348328]
  We introduce for non-uniform messages a novel hybrid universal network coding cryptosystem (NU-HUNCC) We show that NU-HUNCC is information-theoretic individually secured against an eavesdropper with access to any subset of the links.
  arXiv  Detail & Related papers  (2024-02-13T12:12:39Z)
• HasTEE+ : Confidential Cloud Computing and Analytics with Haskell [50.994023665559496]
  Confidential computing enables the protection of confidential code and data in a co-tenanted cloud deployment using specialized hardware isolation units called Trusted Execution Environments (TEEs) TEEs offer low-level C/C++-based toolchains that are susceptible to inherent memory safety vulnerabilities and lack language constructs to monitor explicit and implicit information-flow leaks. We address the above with HasTEE+, a domain-specific language (cla) embedded in Haskell that enables programming TEEs in a high-level language with strong type-safety.
  arXiv  Detail & Related papers  (2024-01-17T00:56:23Z)
• Blind Evaluation Framework for Fully Homomorphic Encryption and Privacy-Preserving Machine Learning [0.0]
  Blind Evaluation Framework (BEF) is a cryptographically secure programming framework. It enables blind, but correct, execution of programming logic without Interactive Rounds of Decryption and Evaluation (IRDE) This is the first framework to enable both training and inference of machine learning models with FHE without decryption rounds.
  arXiv  Detail & Related papers  (2023-10-19T20:33:02Z)
• An Efficient and Multi-private Key Secure Aggregation for Federated Learning [41.29971745967693]
  We propose an efficient and multi-private key secure aggregation scheme for federated learning. Specifically, we skillfully modify the variant ElGamal encryption technique to achieve homomorphic addition operation. For the high dimensional deep model parameter, we introduce a super-increasing sequence to compress multi-dimensional data into 1-D.
  arXiv  Detail & Related papers  (2023-06-15T09:05:36Z)
• Federated Nearest Neighbor Machine Translation [66.8765098651988]
  In this paper, we propose a novel federated nearest neighbor (FedNN) machine translation framework. FedNN leverages one-round memorization-based interaction to share knowledge across different clients. Experiments show that FedNN significantly reduces computational and communication costs compared with FedAvg.
  arXiv  Detail & Related papers  (2023-02-23T18:04:07Z)
• Scalable Collaborative Learning via Representation Sharing [53.047460465980144]
  Federated learning (FL) and Split Learning (SL) are two frameworks that enable collaborative learning while keeping the data private (on device) In FL, each data holder trains a model locally and releases it to a central server for aggregation. In SL, the clients must release individual cut-layer activations (smashed data) to the server and wait for its response (during both inference and back propagation). In this work, we present a novel approach for privacy-preserving machine learning, where the clients collaborate via online knowledge distillation using a contrastive loss.
  arXiv  Detail & Related papers  (2022-11-20T10:49:22Z)
• ScionFL: Efficient and Robust Secure Quantized Aggregation [36.668162197302365]
  We introduce ScionFL, the first secure aggregation framework for federated learning. It operates efficiently on quantized inputs and simultaneously provides robustness against malicious clients. We show that with no overhead for clients and moderate overhead for the server, we obtain comparable accuracy for standard FL benchmarks.
  arXiv  Detail & Related papers  (2022-10-13T21:46:55Z)
• Scotch: An Efficient Secure Computation Framework for Secure Aggregation [0.0]
  Federated learning enables multiple data owners to jointly train a machine learning model without revealing their private datasets. A malicious aggregation server might use the model parameters to derive sensitive information about the training dataset used. We propose textscScotch, a decentralized textitm-party secure-computation framework for federated aggregation.
  arXiv  Detail & Related papers  (2022-01-19T17:16:35Z)
• RoFL: Attestable Robustness for Secure Federated Learning [59.63865074749391]
  Federated Learning allows a large number of clients to train a joint model without the need to share their private data. To ensure the confidentiality of the client updates, Federated Learning systems employ secure aggregation. We present RoFL, a secure Federated Learning system that improves robustness against malicious clients.
  arXiv  Detail & Related papers  (2021-07-07T15:42:49Z)
• FastSecAgg: Scalable Secure Aggregation for Privacy-Preserving Federated Learning [18.237186837994585]
  A'secure aggregation' protocol enables the server to aggregate clients' models in a privacy-preserving manner. FastSecAgg is efficient in terms of computation and communication, and robust to client dropouts.
  arXiv  Detail & Related papers  (2020-09-23T16:49:02Z)

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.