Businesses using third-party LLMs face privacy risks from exposed prompts. This paper presents Portcullis, a privacy-preserving gateway that safeguards sensitive data while supporting efficient and accurate LLM responses. Portcullis functions as a mediator, anonymizing sensitive data in prompts through parallel substitution, securely interacting with LLMs, and accurately reconstructing responses. It ensures all data processing occurs within secure encrypted memory. The gateway is attested to ensure trustworthiness and protect user privacy. Portcullis is the first of its kind, offering a verifiable and scalable privacy gateway for third-party LLM inferences. We assess Portcullis's efficiency as a confidential container platform, demonstrating that its startup time scales linearly, ensuring scalability. Additionally, we evaluate its runtime performance using the PII and Enron Email Dataset. For masking and unmasking workloads, Portcullis outperforms Hide-and-Seek by 96x speed up, while maintaining equal or better false positive and false negative rates compared to existing solutions. On the Enron dataset, Portcullis achieves notably higher accuracy, surpassing Hide-and-Seek by over 0.1 for GPT-4o mini.

One of the paradigms under which split learning (SL) is used is for the vertical federated learning (VFL) setting, where two or more parties build models over feature-partitioned data. However, to protect the private labels of one party, random noises are needed to perturb the backward derivatives (i.e., gradients w.r.t. forward activations), which incurs the privacy-utility tradeoff. In this work, we introduce PROJPERT,, a novel algorithm that searches for the optimal “perturbation knobs” for label protection in SL-based VFL. We formulate the problem of perturbation searching as how to minimize the impact on model quality given the desired privacy guarantee. Based on the problem, two solutions are introduced, where the first obtains the optimal perturbation via a simple but effective binary searching scheme, and the second heuristically approximates the optimality within a negligible error bound. Empirical results demonstrate that both our solutions are more effective in protecting the labels and achieve significantly better privacy-utility tradeoffs than state-of-the-art perturbation-based label protection methods. Furthermore, our heuristic solution is very efficient and incurs almost zero extra overhead in the overall running time, improving the usability in real-world applications.

Due to the rising concerns on privacy protection, how to build machine learning (ML) models over different data sources with security guarantees is gaining more popularity. Vertical federated learning (VFL) describes such a case where ML models are built upon the private data of different participated parties that own disjoint features for the same set of instances, which fits many real-world collaborative tasks. Nevertheless, we find that existing solutions for VFL either support limited kinds of input features or suffer from potential data leakage during the federated execution. To this end, this paper aims to investigate both the functionality and security of ML modes in the VFL scenario.

Vertical federated learning (VFL) is an emerging paradigm that allows different parties (e.g., organizations or enterprises) to collaboratively build machine learning models with privacy protection. In the training phase, VFL only exchanges the intermediate statistics, i.e., forward activations and backward derivatives, across parties to compute model gradients. Nevertheless, due to its geo-distributed nature, VFL training usually suffers from the low WAN bandwidth. In this paper, we introduce CELU-VFL, a novel and efficient VFL training framework that exploits the local update technique to reduce the cross-party communication rounds. CELU-VFL caches the stale statistics and reuses them to estimate model gradients without exchanging the ad hoc statistics. Significant techniques are proposed to improve the convergence performance. First, to handle the stochastic variance problem, we propose a uniform sampling strategy to fairly choose the stale statistics for local updates. Second, to harness the errors brought by the stateness, we devise an instance weighting mechanism that measures the reliability of the estimated gradients. Theoretical analysis proves that CELU-VFL achieves a similar sub-linear convergence rate as vanilla VFL training but requires much fewer communication rounds. Empirical results on both public and real-world workloads validate that CELU-VFL can be up to six times faster than the existing works.

With the ever-evolving concerns on privacy protection, vertical federated learning (FL), where participants own non-overlapping features for the same set of instances, is becoming a heated topic since it enables multiple enterprises to strengthen the machine learning models collaboratively with privacy guarantees. Nevertheless, to achieve privacy preservation, vertical FL algorithms involve complicated training routines and time-consuming cryptography operations, leading to slow training speed.