Privacy

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Period ending 2026-09-21

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AI Privacy Risks and Protection

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Differential Privacy

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Privacy-Preserving Framework

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Homomorphic Encryption

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Efficient Privacy Loss

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Privacy-Utility Trade-Offs

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Diffie-Hellman

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559 papers

Latest in Privacy

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Split-n-Chain: Privacy-Preserving Multi-Node Split Learning with Blockchain-Based Auditability
Mar 10, 2025Mukesh Sahani, Binanda SenguptaFederated Learning Privacy-Preserving Framework
GDBR: Label Recovery Attack Against Partial Gradient Encryption in Federated Learning
Dec 17, 2024Rui Zhang, Ka-Ho ChowFederated Learning Homomorphic Encryption
Batch effects can impair federated learning in multi-center omics studies
Dec 8, 2024Yuliya Burankova, Julian Klemm, Jens J. G. Lohmann +5Federated Learning Multi-Omic Framework
Trust-free Personalized Decentralized Learning
Oct 15, 2024Yawen Li, Yan Li, Junping Du +3Privacy-Preserving Framework Fedavg
The EM-algorithm and the Method of Moments in Softmax Mixture Models
Sep 16, 2024Xin Bing, Florentina Bunea, Jonathan Niles-Weed +1Mixture Models Expectation-Maximization
Tempora-Fusion: Time-Lock Puzzle with Efficient Verifiable Homomorphic Linear Combination
Jun 21, 2024Aydin Abadi, Jakub K. SzelagHomomorphic Encryption Zero-Knowledge Proofs
Privacy-Aware Visual Language Models
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Personalized Additive Modeling for Multi-level Federated Learning
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FedReview: Review and Dispose Poisoned Updates without Validation Datasets or Historic Knowledge
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A Differentially Private Weighted Empirical Risk Minimization Procedure and its Application to Outcome Weighted Learning
Jul 24, 2023Spencer Giddens, Yiwang Zhou, Kevin R. Krull +3Differential Privacy Empirical Risk Minimization
Privacy-Preserving Logistic Regression Training with A Faster Gradient Variant
Jan 26, 2022John ChiangPrivacy-Preserving Framework Gradient Descent
DNA: Differentially private Neural Augmentation for contact tracing
Date pendingRob Romijnders, Christos Louizos, Yuki M. Asano +1Covid-19 Differential Privacy
PEARL: Structural Privacy-Utility Control in Human-Centric CPS via Personalized Early-Exit Deep Reinforcement Learning
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Capable but Careless: Do Computer-Use Agents Follow Contextual Integrity?
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DP-Muon: Differentially Private Optimization via Matrix-Orthogonalized Momentum
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BodhiPromptShield: Pre-Inference Prompt Mediation for Surface-Form Privacy Propagation in LLM Agent Pipelines
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Demystifying the Privacy-Utility Trade-off in LLM Interactions
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Mar 10, 2025 · cs.CRJ/K move · Enter open · S save

Split-n-Chain: Privacy-Preserving Multi-Node Split Learning with Blockchain-Based Auditability

Mukesh Sahani, Binanda Sengupta

Indian Institute of Technology (Indian School of Mines) Dhanbad, India · Indian Institute of Engineering Science and Technology, Shibpur, India

Deep learning, when integrated with a large amount of training data, has the potential to outperform machine learning in terms of high accuracy. Recently, privacy-preserving deep learning has drawn significant attention of the research community. Different privacy notions in deep learning include privacy of data provided by data-owners and privacy of parameters and/or hyperparameters of the underlying neural network. Federated learning is a popular privacy-preserving execution environment where data-owners participate in learning the parameters collectively without leaking their respective data to other participants. However, federated learning suffers from certain security/privacy issues. In this paper, we propose Split-n-Chain, a variant of split learning where the layers of the network are split among several distributed nodes. Split-n-Chain achieves several privacy properties: data-owners need not share their training data with other nodes, and no nodes have access to the parameters and hyperparameters of the neural network (except that of the respective layers they hold). Moreover, Split-n-Chain uses blockchain to audit the computation done by different nodes. Our experimental results show that: Split-n-Chain is efficient, in terms of time required to execute different phases, and the training loss trend is similar to that for the same neural network when implemented in a monolithic fashion.

Federated Learning Privacy-Preserving Framework Deep Learning Algorithm Auditing

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