Aloepri: In-depth Analysis of Privacy-Preserving Large Language Model Inference Technology Based on Collaborative Obfuscation

Aloepri is a cutting-edge research on privacy-preserving large language model inference, aiming to solve the privacy dilemma where user data needs to be sent to the cloud for processing in large model applications. Through an innovative collaborative obfuscation architecture, this technology completes model inference without exposing users' original inputs, achieving a balance between privacy protection and model inference capabilities, and opening up a practical path for large model applications in sensitive fields such as healthcare and finance.

Large language model applications face a core contradiction: leveraging model capabilities requires sending raw data to the cloud, which brings privacy risks in sensitive fields. Existing privacy protection solutions have limitations: homomorphic encryption has high computational overhead, secure multi-party computation has high communication costs, and local deployment is limited by device computing power. As a lightweight method, obfuscation technology has emerged, and Aloepri innovatively combines it with collaborative computing to build a practical framework.

Aloepri adopts a unique collaborative architecture: users' original inputs are not directly sent to service providers, but are processed through collaborative obfuscation by multiple users, with each other's data covering one another to enhance privacy. It proposes a layered obfuscation strategy: semantic layer (synonym replacement, sentence structure reconstruction), representation layer (embedding space transformation), and computation layer (distributed computing to disperse sensitive information), which can flexibly adjust the protection strength. The balance between privacy and inference utility is optimized through learnable obfuscation parameters.

Various obfuscation functions are designed (adversarial example ideas, differential privacy perturbation, semantic-preserving transformation, etc.), which can be combined to address different attacks. A key exchange protocol is implemented based on public key infrastructure, supporting Byzantine fault tolerance to deal with malicious participants. The model output needs to go through a multi-party collaborative result mapping mechanism to restore from the obfuscated space to the original semantic space, ensuring that no single participant can obtain complete information.

Multiple attack models are defined (honest-but-curious service providers, malicious participants, external eavesdroppers), and security boundaries are analyzed. Information-theoretic quantitative indicators are introduced, and experiments show that it can effectively resist attacks such as membership inference, attribute inference, and model inversion. Compared with traditional solutions, it has significant advantages in computational efficiency and communication overhead, with acceptable inference latency and privacy protection level close to the theoretical optimum.

Healthcare consultation: Patients use Aloepri to consult AI about symptoms; the obfuscated queries ensure accurate AI suggestions while protecting the privacy of their conditions. Enterprise data analysis: Using cloud-based large models to analyze internal documents without leaking commercial secrets. Personal assistant services: Sensitive information such as daily queries and schedules is sent after obfuscation to reduce the risk of leakage.

Current obfuscation solutions are targeted at specific models, so it is necessary to improve generalization ability for different models. Static strategies are easy to crack, so adaptive obfuscation mechanisms need to be studied to deal with dynamic attacks. Collaborative interaction may affect the experience, so it is necessary to simplify the process, lower the threshold, and improve response speed.

Aloepri represents an important progress in the field of privacy-preserving large model inference. It balances privacy and utility through a collaborative obfuscation architecture, providing a feasible solution for applications in sensitive scenarios. The project open-sources code, datasets, and technical reports to promote community research and standardization, and its lightweight collaborative obfuscation approach is expected to be widely applied in the future.