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  • Secure Data Aggregation | Springer Nature Link
    As an approach to protecting data confidentiality privacy in aggregation, He et al ( 2007) proposed the cluster-based private data aggregation (CPDA) scheme and the slice-mix-aggregate (SMART) scheme Both schemes rely on the collaboration between neighboring nodes to hide the actual data reported by each individual node
  • Secure aggregate signature scheme for smart city applications
    Consequently, creating a secure and effective data aggregation protocol is very important for smart city applications This paper, a secure aggregate signature scheme in smart city applications, aims to aggregate signatures from multi-application into one signature, which can secure the city’s network from malicious adversaries
  • SoK: Secure aggregation based on cryptographic schemes for . . . - EURECOM
    In this work, we study the suitability of secure aggregation based on cryptographic schemes to federated learning We first provide a formal definition of the problem and suggest a systematic categorization of existing solutions
  • Verifiable secure aggregation scheme for privacy protection in . . .
    To verify the integrity of server aggregation computations while protecting the privacy of clients, this paper introduces a privacy-preserving verifiable secure aggregation scheme for federated learning networks
  • SoK: Secure Aggregation Based on Cryptographic Schemes for Federated . . .
    We further investigate the specific challenges raised by federated learning and analyze the recent dedicated secure aggregation solutions based on cryptographic schemes We finally share some takeaway messages that would help a secure design of federated learning and identify open research directions in this topic
  • An effective and verifiable secure aggregation scheme with privacy . . .
    This paper presents a secure aggregation scheme that addresses privacy protection and verifiability in federated learning Firstly, a new homomorphic signature algorithm has been used to verify the aggregation results
  • Practical Secure Aggregation for Federated Learning on User-Held Data
    In this work, we consider training a deep neural network in the Federated Learning model, using distributed gradient descent across user-held training data on mobile devices, using Secure Aggregation to protect the privacy of each user’s model gradient
  • Secure Aggregation Protocol Based on DC-Nets and Secret Sharing for . . .
    We propose a secure aggregation protocol for Decentralized Federated Learning, which does not require a central server to orchestrate the aggregation process To achieve this, we combined a Multi-Secret-Sharing scheme with a Dining Cryptographers Network We validate the proposed protocol in simulations using the MNIST handwritten digits dataset
  • SAFE: Secure Aggregation with Failover and Encryption
    ElGamal encryption, by its efficiency, is one of the most used schemes in cryptographic applications However, the original ElGamal scheme is only provably secure against passive attacks
  • DealSecAgg: Efficient Dealer-Assisted Secure Aggregation for Federated . . .
    We propose the efficient secure aggregation protocol DealSecAgg The cryptographic scheme is based on a lightweight single-masking approach and allows the aggregation of the global model under encryption
  • Secure Aggregation in Federated Learning using Multiparty Homomorphic . . .
    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, while receiving only encrypted version of each individual gradients Towards this end, we extend classical MPHE methods so that the decryption of the
  • Contract-based hierarchical security aggregation scheme for enhancing . . .
    Bonawitz et al [18] proposed a double-masking scheme that uses Shamir secret sharing and a series of cryptographic techniques to achieve secure aggregation against semi-honest and malicious attackers, as well as embedding protection against user loss through threshold Shamir secret sharing
  • Secure Post-Quantum Cryptographic Framework Using Hybrid Lattice-Based . . .
    An adaptive aggregation approach dynamically scales model updates based on node reliability and threat intensity, improving resilience to poisoning and inference attacks Benchmark post-quantum parameters and public intrusion datasets, such as UNSW-NB15 and CICIDS2017 are tested in the framework in a simulated smart city setting





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