Inference Attacks Against Collaborative Learning

By Luca Melis, Congzheng Song, Emiliano De Cristofaro, Vitaly Shmatikov. 2018

110 citations recorded by the source corpus.

Subfields: Federated & Distributed Learning, Privacy & Security

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In Privacy & Security

Citations in the corpus

Federated Machine Learning (2019, background): “ors demonstrate that it is possible to insert hidden backdoors into a joint global model and propose a new "constrain-and-scale" model-poisoning methodology to reduce the data poisoning. In [43], researchers identified potential loopholes in collaborative machine learning systems, where th”
LEAF: A Benchmark for Federated Settings (2018, background): “, FaceScrub in [19], Shakespeare in [16] and Reddit in [10, 18, 3].”
Protection Against Reconstruction and Its Applications in Private Federated Learning (2018, background): “Yet even if only updates to a model are transmitted, leaving all user or participant data on user-owned devices, it is easy to compromise the privacy of users [37, 57].”
Biscotti: A Ledger for Private and Secure Peer-to-Peer Machine Learning (2018, methodology): “ain noise to hide their updates by following the noising protocol. 4.4 Noising protocol Sending a peer’s SGD update directly to another peer may leak sensitive information about their private dataset [33]. To prevent this, peers use differential privacy to hide their updates prior to veriﬁcation by ”
Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning (2018, background): “However, recent works [4], [6], which only accessed the aggregated updates, could compromise the clients’ privacy by introducing adversarial influence.”
LOGAN: Membership Inference Attacks Against Generative Models (2017, background): “[41] demonstrate how an adversarial participant can successfully perform membership inference in distributed learning [39, 56], as well as inferring sensitive properties that hold only for a subset of the participants’ training data.”
Gradient-Leaks: Understanding and Controlling Deanonymization in Federated Learning (2018, methodology): “To keep the background in this section concise, we present key concepts to understand: (i) how devices generate model parameter updates using the FederatedAveraging [55] algorithm; and (ii) how users anonymously communicate the parameter updates to the server in FL [15,56,59].”
MLCapsule: Guarded Offline Deployment of Machine Learning as a Service (2018, background): “Local execution also avoids the Internet communication between the user and the MLaaS provider and eliminates possible attack surfaces due to network communication [20, 24].”
Stolen Memories: Leveraging Model Memorization for Calibrated White-Box Membership Inference (2019, background): “(GANs); and others [16, 30, 33] have studied membership inference in the context of collaborative, or federated, learning.”
ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models (2018, background): “Besides the above, other recent works on security and privacy in machine learning include [21], [28], [37]”
Preserving Data Privacy via Federated Learning: Challenges and Solutions (2020, background): “Illustration of the inference attacks against collaboration learning.(9)”
FedLoc: Federated Learning Framework for Data-Driven Cooperative Localization and Location Data Processing (2020, background): “However, recent studies have shown that the shared parameters of the trained models are proved to be vulnerable to disclose sensitive information [97].”
Secure Aggregation with Heterogeneous Quantization in Federated Learning (2020, methodology): “Hence, this strategy is not robust against some attacks such as Membership Inference Attack [24], [25].”
DeepChain: Auditable and Privacy-Preserving Deep Learning with Blockchain-Based Incentive (2019, background): “Although in distributed deep training each party only uploads her local gradients to the parameter server, still adversaries can infer through those gradients important information about the party’s local data by initiating an inference attack or membership attack [18].”
Differentially Private Federated Learning: An Information-Theoretic Perspective (2021, background): “Although users never share their local data directly during each iteration — only gradients are transmitted — FL can still compromise user privacy [2, 3].”
Detecting AI Trojans Using Meta Neural Analysis (2019, background): “Property inference attacks [3], [20], [43] aim to infer certain properties about the training dataset or the model of a target model.”
iDLG: Improved Deep Leakage from Gradients (2020, background): “In multi-node distributed learning systems such as Collaborative Learning [2, 3, 4] and Federated Learning [5, 6, 7], it is widely believed that sharing gradients between nodes will not leak the private training data.”
Privacy preservation in federated learning: An insightful survey from the GDPR perspective (2020, background): “Authors in [85, 86, 94] have investigated membership attacks in FL and demonstrated the capability of these attacks in both passive and active approaches.”