# ObCLIP: Oblivious CLoud-Device Hybrid Image Generation with Privacy Preservation

arXiv: https://arxiv.org/abs/2510.04153 · HTML: https://mlsanity.com/paper/2510.04153/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Robustness, privacy and adversarial aspects, one of 14 directions in Diffusion models.
- Builds on 30 of its 47 references that are in the map.
- First posted on arXiv 5 Oct 2025.

## Placed in

- Diffusion models / [Robustness, privacy and adversarial aspects](https://mlsanity.com/diffusion/robustness-privacy-and-adversarial-aspects/)

## Builds on (most cited first, 30 of 30)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [CLIPScore: A Reference-free Evaluation Metric for Image Captioning](https://mlsanity.com/paper/2104.08718/) (2021)
- [DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models](https://mlsanity.com/paper/2211.01095/) (2022)
- [eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers](https://mlsanity.com/paper/2211.01324/) (2022)
- [Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference](https://mlsanity.com/paper/2310.04378/) (2023)
- [DeepCache: Accelerating Diffusion Models for Free](https://mlsanity.com/paper/2312.00858/) (2023)
- [SnapFusion: Text-to-Image Diffusion Model on Mobile Devices within Two Seconds](https://mlsanity.com/paper/2306.00980/) (2023)
- [Structural Pruning for Diffusion Models](https://mlsanity.com/paper/2305.10924/) (2023)
- [Text Embeddings Reveal (Almost) As Much As Text](https://mlsanity.com/paper/2310.06816/) (2023)
- [Diffusion Probabilistic Model Made Slim](https://mlsanity.com/paper/2211.17106/) (2022)
- [Differential Privacy for Text Analytics via Natural Text Sanitization](https://mlsanity.com/paper/2106.01221/) (2021)
- [Faster Diffusion via Temporal Attention Decomposition](https://mlsanity.com/paper/2404.02747/) (2024)
- [PUMA: Secure Inference of LLaMA-7B in Five Minutes](https://mlsanity.com/paper/2307.12533/) (2023)
- [MobileDiffusion: Instant Text-to-Image Generation on Mobile Devices](https://mlsanity.com/paper/2311.16567/) (2023)
- [On the Scalability of Diffusion-based Text-to-Image Generation](https://mlsanity.com/paper/2404.02883/) (2024)
- [Faster Diffusion: Rethinking the Role of the Encoder for Diffusion Model Inference](https://mlsanity.com/paper/2312.09608/) (2023)
- [MPCViT: Searching for Accurate and Efficient MPC-Friendly Vision Transformer with Heterogeneous Attention](https://mlsanity.com/paper/2211.13955/) (2022)
- [KOALA: Empirical Lessons Toward Memory-Efficient and Fast Diffusion Models for Text-to-Image Synthesis](https://mlsanity.com/paper/2312.04005/) (2023)
- [MMDT: Decoding the Trustworthiness and Safety of Multimodal Foundation Models](https://mlsanity.com/paper/2503.14827/) (2025)
- [Hybrid SD: Edge-Cloud Collaborative Inference for Stable Diffusion Models](https://mlsanity.com/paper/2408.06646/) (2024)
- [Ditto: Quantization-aware Secure Inference of Transformers upon MPC](https://mlsanity.com/paper/2405.05525/) (2024)
- [Universal Zero-shot Embedding Inversion](https://mlsanity.com/paper/2504.00147/) (2025)
- [Privacy-Preserving Diffusion Model Using Homomorphic Encryption](https://mlsanity.com/paper/2403.05794/) (2024)
- [Cape: Context-Aware Prompt Perturbation Mechanism with Differential Privacy](https://mlsanity.com/paper/2505.05922/) (2025)

## Abstract (arXiv)

Diffusion Models have gained significant popularity due to their remarkable capabilities in image generation, albeit at the cost of intensive computation requirement. Meanwhile, despite their widespread deployment in inference services such as Midjourney, concerns about the potential leakage of sensitive information in uploaded user prompts have arisen. Existing solutions either lack rigorous privacy guarantees or fail to strike an effective balance between utility and efficiency. To bridge this gap, we propose ObCLIP, a plug-and-play safeguard that enables oblivious cloud-device hybrid generation. By oblivious, each input prompt is transformed into a set of semantically similar candidate prompts that differ only in sensitive attributes (e.g., gender, ethnicity). The cloud server processes all candidate prompts without knowing which one is the real one, thus preventing any prompt leakage. To mitigate server cost, only a small portion of denoising steps is performed upon the large cloud model. The intermediate latents are then sent back to the client, which selects the targeted latent and completes the remaining denoising using a small device model. Additionally, we analyze and incorporate several cache-based accelerations that leverage temporal and batch redundancy, effectively reducing computation cost with minimal utility degradation. Extensive experiments across multiple datasets demonstrate that ObCLIP provides rigorous privacy and comparable utility to cloud models with slightly increased server cost.