# SharedRequest: A Batch-Level Privacy-Preserving Inference Framework with 5x Cost Reduction

> SharedRequest reduces query costs by 5x while protecting user prompt privacy through batch-level privacy preservation and semantic instruction grouping, without the need to modify model architectures.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-06-03T15:23:06.000Z
- 最近活动: 2026-06-04T05:20:01.341Z
- 热度: 131.1
- 关键词: 隐私保护, 模型推理, 差分隐私, 批量处理, LLM安全
- 页面链接: https://www.zingnex.cn/en/forum/thread/sharedrequest-5
- Canonical: https://www.zingnex.cn/forum/thread/sharedrequest-5
- Markdown 来源: floors_fallback

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## SharedRequest: Core Guide to the Batch-Level Privacy-Preserving Inference Framework

SharedRequest is a batch-level privacy-preserving inference framework. It reduces query costs by 5x while protecting user prompt privacy through semantic instruction grouping and batch-level privacy preservation mechanisms, without modifying model architectures. Its core idea is to shift privacy protection from the single-prompt level to the batch level, achieving a balance between privacy, utility, and efficiency. It is applicable to various LLMs (including closed-source APIs and open-source models).

## Problem Background: The Dilemma of Privacy-Preserving Inference

With the widespread application of public LLMs like ChatGPT, the risk of user prompt privacy leakage has become increasingly prominent. Existing privacy-preserving inference methods have many issues: differential privacy adds noise, sacrificing output utility; homomorphic encryption/secure multi-party computation has huge overhead; model-specific solutions require architecture modifications and lack generality. How to protect privacy while maintaining efficiency, generality, and not affecting output quality has become a challenge.

## Core Methods of SharedRequest: Batch-Level Privacy Preservation and Semantic Grouping

### Core Idea
Shift privacy protection from the single-prompt level to the batch level, amortize costs through semantic equivalent instruction grouping, and obfuscate sensitive information by mixing noise variants.

### Technical Mechanisms
1. **Semantic Instruction Grouping**: Identify semantically similar queries and group them together, sharing instruction templates;
2. **Noise Mixing Obfuscation**: Generate multiple noise variants and mix them with the original prompt to protect the real content;
3. **Batch Amortized Inference**: Process the shared instruction part in batches, efficiently deliver personalized content, and linearly amortize costs.

### Model Agnosticism
No need to access model parameters or modify architectures; it runs as a black-box API wrapper layer, can be seamlessly integrated into existing workflows, and is applicable to closed-source APIs, open-source hosting services, and privately deployed models.

## Experimental Results: Win-Win Verification of Privacy and Efficiency

### Utility Improvement
Compared to traditional differential privacy baselines, output quality is improved by over 20%, and semantic coherence is close to the unprotected baseline.

### Cost Reduction
Query costs are reduced by up to 5x (significant in large-batch scenarios), latency is optimized (reducing network round trips), and throughput is improved.

### Privacy Strength
The noise mixing mechanism effectively defends against external eavesdroppers, supports privacy-utility trade-offs, and conforms to the differential privacy theoretical framework.

## Application Scenarios and Deployment Considerations

### Applicable Scenarios
1. Enterprise-level API proxies (privacy-protected access for internal employees);
2. Privacy-sensitive industries such as healthcare, finance, and law;
3. High-concurrency public interfaces;
4. General solutions for multi-cloud deployment.

### Deployment Recommendations
- Tune batch size (balance latency and cost);
- Optimize domain-specific semantic grouping strategies;
- Calibrate noise intensity (match privacy requirements);
- Establish privacy protection effect monitoring and auditing mechanisms.

## Limitations and Future Research Directions

### Current Limitations
- Batch processing may introduce latency that affects real-time applications;
- The accuracy of semantic grouping for complex queries needs improvement;
- Need to defend against advanced adversarial attacks targeting specific patterns.

### Future Directions
- Adaptive batch strategy (dynamically adjust size);
- Hierarchical privacy protection (differentiated processing of sensitive content);
- Integration with federated learning;
- Hardware acceleration to improve batch processing efficiency.

## Conclusion: The Value and Significance of SharedRequest

SharedRequest represents an important advancement in privacy-preserving LLM inference, balancing privacy, utility, and efficiency through batch-level privacy protection. In today's era where data privacy is valued, its model-agnostic, efficient, and practical features have important application value for organizations that need to deploy LLMs at scale and meet privacy compliance requirements, providing a technical path worth considering.