The paper introduces the Homomorphic Advantage Operator (HAO), a stabilization framework for reinforcement learning under fully homomorphic encryption (FHE). HAO applies a zero‑mean centering projection to temporal‑difference targets, eliminating the uniform state‑value baseline that causes the Bellman drift and keeping network pre‑activations within safe polynomial approximation bounds. Experiments on tabular MDPs, an encrypted CartPole environment, and a logistics routing benchmark show that HAO prevents boundary breaches entirely, improves optimal policy accuracy by 18 percentage points, and remains stable even with added Gaussian noise.
arXiv:2509.01253v2 Announce Type: replace-cross Abstract: In contemporary cloud-based services, protecting users' sensitive data and ensuring the confidentiality of the server's model are critical. F...
HEAT introduces a fine‑tuning method that treats the number of iterations used to approximate nonlinearities in fully homomorphic encryption (FHE) as learnable parameters, allowing them to co‑adapt with model weights. By optimizing iteration counts per nonlinearity, HEAT reduces the required iterations, bootstraps, and overall latency for encrypted GPT‑2 decoding while improving decode agreement. The approach achieves a 3.1× reduction in iterations, a 1.6× reduction in bootstraps, and a 1.4× speed‑up in end‑to‑end latency without changing the model architecture or requiring retraining from scratch.
PEARL is a framework for human‑centric cyber‑physical systems that uses a dual‑path Early‑Exit Deep Q‑Network to control the trade‑off between privacy and utility. By training per‑branch binary labels—Utility Confidence Labels (UCL) and Privacy Confidence Labels (PCL)—based on mutual information between private states and observable actions, PEARL selects the shallowest exit that satisfies both privacy and utility constraints, avoiding noise injection. The system includes an MI‑based feedback loop to detect behavioral drift and trigger retraining, and experiments on a smart‑home HVAC system and a VR smart classroom show a 25.67% reduction in adversarial state‑inference accuracy with only a 10‑16% utility cost.
arXiv:2609.13636v1 Announce Type: cross Abstract: Privacy-preserving inference via Torus Fully Homomorphic Encryption (TFHE) provides strong protection for sensitive data in outsourced deep learning...
arXiv:2609. 01945v1 Announce Type: cross Abstract: Federated Learning enables multiple clients to train a shared model while keeping their local datasets isolated.
arXiv:2606. 16359v1 Announce Type: cross Abstract: Fully Homomorphic Encryption (FHE) enables privacy-preserving machine learning but incurs extreme computational and memory overhead.
SecureDrive‑FL combines differential privacy (DP‑SGD) with a novel Gradient‑Aware Selective Homomorphic Encryption (GASHE) scheme to protect federated driver‑monitoring models. GASHE encrypts only gradient components that exceed a DP‑calibrated sensitivity threshold, avoiding full‑parameter encryption. In experiments on a ten‑class distracted driver task, SecureDrive‑FL matches DP‑SGD’s poisoning resistance while also defending against Man‑in‑the‑Middle attacks, adding only 8–10% runtime overhead.
arXiv:2606. 25627v1 Announce Type: new Abstract: Distributed intelligent systems increasingly need to train across data silos without centralizing raw data.
The paper introduces GASHE, a gradient‑aware selective homomorphic encryption scheme that encrypts only those gradient components exceeding a differential‑privacy‑calibrated sensitivity threshold, rather than encrypting all parameters. Building on GASHE, SecureDrive‑FL combines DP‑SGD with this selective encryption to form a closed‑loop DP+HE privacy pipeline for federated driver monitoring. Experiments on a ten‑class distracted driver classification task show that SecureDrive‑FL retains the poisoning resistance of DP‑SGD while also defending against Man‑in‑the‑Middle interception, with only an 8–10% runtime overhead.
arXiv:2607. 04819v1 Announce Type: new Abstract: Fully homomorphic encryption (FHE) enables computation on encrypted data, but practical encrypted Transformer inference is bottlenecked by the sequential composition of many nonlinear blocks.
The paper demonstrates that by choosing unit‑quaternion coordinates, encrypted updates for variational quantum circuits become bilinear, allowing depth‑one homomorphic federated learning without bootstrapping. This coordinate choice reduces encrypted rotation updates to a single multiplicative level and federated averaging to zero levels, eliminating the previously prohibitive cost of one round per gate. Experiments across two cryptographic backends and up to 20 clients show negligible aggregation error and no measurable loss in utility, with hardware validation on a 156‑qubit processor achieving near‑optimal fidelity.
arXiv:2605. 26903v2 Announce Type: replace-cross Abstract: Structured data is well handled by gradient-boosted decision trees (GBDT), which are usually trained on vertically partitioned features across mutually distrustful parties.