Black-box tuning is an emerging paradigm for adapting large language models (LLMs) to better achieve desired behaviors, particularly when direct access to model parameters is unavailable. Current strategies, however, often present a dilemma of suboptimal extremes: either separately train a small proxy model and then use it to shift the predictions of the foundation model, offering notable efficiency but often yielding limited improvement; or making API calls in each tuning iteration to the foundation model, which entails prohibitive computational costs. In this paper, we argue that a more reasonable way for black-box tuning is to train the proxy model with limited API calls. The underlying intuition is based on two key observations: first, the training samples may exhibit correlations and redundancies, suggesting that the foundation model’s predictions can be estimated from previous calls; second, foundation models frequently demonstrate low accuracy on downstream tasks. Therefore, we propose a novel advanced black-box tuning method for LLMs with limited API calls. Our core strategy involves training a Gaussian Process (GP) surrogate model with "LogitMap Pairs" derived from querying the foundation model on a minimal but highly informative training subset. This surrogate can approximate the outputs of the foundation model to guide the training of the proxy model, thereby effectively reducing the need for direct queries to the foundation model. Extensive experiments verify that our approach elevates pre-trained language model accuracy from 55.92% to 86.85%, reducing the frequency of API queries to merely 1.38%. This significantly outperforms offline approaches that operate entirely without API access. Notably, our method also achieves comparable or superior accuracy to query-intensive approaches, while significantly reducing API costs. This offers a robust and high-efficiency paradigm for language model adaptation.

The Paillier cryptosystem is renowned for its applications in electronic voting, threshold ECDSA, multi-party computation, and more, largely due to its additive homomorphism. In these applications, range proofs for the Paillier cryptosystem are crucial for maintaining security, because of the mismatch between the message space in the Paillier system and the operation space in application scenarios.

Online/offline identity-based signature (OO-IBS) is an adequate cryptographic tool to provide the message authentication and integrity in mobile devices, since it lightens the computational burden after the signer receives the message and eliminates the overhead of certificate management. It has several valuable applications, such as wireless sensor networks and automatic dependent surveillance-broadcast systems. Identity-based chameleon hash (IB-CH), as an alternative building block to construct OO-IBS, has been explored in several literatures. Nevertheless, almost all of the prior IB-CH schemes are in the random oracle model, which may lead to security risks in practicality. The only IB-CH scheme in the standard model proposed by Xie et al. (ICC’21) suffers from the large size of public parameters and inefficient setup process. In this paper, we propose an efficient IB-CH scheme in the standard model, significantly reducing the computational costs of all the algorithms and the size of public parameters compared with Xie’s scheme. The security and experimental analyses demonstrate the security and good performance of our scheme. Furthermore, we applied our scheme to optimize the existing generic OO-IBS construction. Our optimized construction reduces computational overhead by 50.0% in the online phase compared with the original construction.

The rapid development of the mobile Internet makes it necessary to adopt efficient cryptographic primitives for the portable devices with limited computing resources. Online/offline identity-based signatures are suitable because of short response time of signature generation and being free from the cumbersome operations caused by public key infrastructures. In this paper, we propose the first identity-based chameleon hash which can be proved secure without the random oracle and show how to use it to translate any identity-based signature to an online/offline one.

Abstract Attribute-based encryption with equality test (ABEET) simultaneously supports fine-grained access control on the encrypted data and plaintext message equality comparison without decrypting the ciphertexts. Recently, there have been several literatures about ABEET proposed. Nevertheless, most of them explore the ABEET schemes in the random oracle model, which has been pointed out to have many defects in practicality. The only existing ABEET scheme in the standard model, proposed by Wang et al., merely achieves the indistinguishable against chosen-plaintext attack security. Considering the aforementioned problems, in this paper, we propose the first direct adaptive chosen-ciphertext security ciphertext-policy ABEET scheme in the standard model. Our method only adopts a chameleon hash function and adds one dummy attribute to the access structure. Compared with the previous works, our scheme achieves the security improvement, ciphertext validity check and large universe. Besides, we further optimize our scheme to support the outsourced decryption. Finally, we first give the detailed theoretical analysis of our constructions in computation and storage costs, then we implement our constructions and carry out a series of experiments. Both results indicate that our constructions are more efficient in Setup and Trapdoor and have the shorter public parameters than the existing ABEET ones do.