A blockchain is a distributed ledger that allows users to exchange information without a centralized authority. This technology enables users to send and receive tokens among other applications, such as transactions, product management, and elections. It is possible to send data and tokens inside a single blockchain, but a method to efficiently share the data and tokens among different blockchains has not yet been constructed. Cross-chain communication, the focal point of several recent research efforts, is a scheme for sending data or tokens among different blockchains. In existing studies, a trusted third party (TTP) is used to ensure fair rates of token exchange among different blockchains. However, because blockchains are originally designed with a policy that does not incorporate the use of TTPs, the fair exchange rate should not be determined by TTPs, but rather by the market price of tokens among users. When exchange rates are determined from quotes among users, the preferred scheme is to determine the exchange rate offered by many users as an auction. Here, some existing cross-chain communication systems use smart contracts that automatically execute arbitrary processes on the blockchain. However, such schemes require a gas fee each time a smart contract is executed. Thus, implementing an auction scheme that determines the fair exchange rate among different blockchains would necessitate each user to pay a fee for each new token offered, which would result in high gas fees. In this study, we propose a scheme to determine exchange rates from quotes among users with a relatively low gas fee. Using a first-price sealed-bid auction and commitment scheme, the user with the highest token value can be identified without revealing the other users’ token offer values. In our scheme, the largest token value among users is determined as the exchange rate using an external Smart Contract (SC) instead of a TTP. We further modify the existing insert key-value commitment scheme to aggregate the commitment values of token offers. Our scheme is based on the generalized RSA assumption. By proving that it satisfies the key-binding property, we prove that the token sender cannot act maliciously. We further implement the proposed scheme and demonstrate that the gas fees and data space required to implement the proposed scheme are practically feasible. Copyright © 2026 The Institute of Electronics, Information and Communication Engineers.

A blockchain is an important component in the design of secure distributed file systems, such as cryptocurrencies. One of the key components of the blockchain is the key-value commitment scheme, which constructs a commitment value from two inputs: a key and a value. In a conventional commitment scheme, a single user constructs a commitment value from an input value, whereas in a key-value commitment scheme, multiple users construct a commitment value from their keys and values. Both conventional and key-value commitment schemes must satisfy binding and hiding properties. The key-binding and key-hiding properties guarantee that neither the sender nor the verifier can act maliciously. The concept of a key-value commitment scheme was first proposed by Agrawal et al. in 2020 using a strong RSA assumption. Their scheme satisfies the key-binding but not key-hiding properties. In this paper, we propose two lattice-based key-value commitment schemes, Insert-KVCm/2,n,q,beta and KVCm,n,q,beta , that satisfy both the key-binding and the key-hiding properties. The key-binding property of both Insert-KVCm/2,n,q,beta and KVCm,n,q,beta are proven under the short integer solution ( SIS infinity n,m,q,beta ) problem. The key-hiding property of both Insert-KVCm/2,n,q,beta and KVCm,n,q,beta are proven under the Decisional- SISn,m,q,beta infinity -form problem, which is newly defined in this paper. We demonstrate the difficulty of the Decisional- SISn,m,q,beta infinity -form problem by showing that the Decisional- SISn,m,q,beta infinity -form problem is secure when the SISn,m,q,beta infinity problem is secure. Finally, we analyze the computational costs of Insert-KVCm/2,n,q,beta and KVCm,n,q,beta . Our method is the first lattice-based key-value commitment scheme with proven the key-binding and the key-hiding properties.

The field of generative AI is currently seeing a surge in text-to-image generation. Among open-source projects, Stable Diffusion stands out as the state-of-the-art. Artists and service providers often customize diffusion models for unique textures. However, there is a lack of privacy protection for users' input text prompts, output images, and customized models on servers. Ensuring privacy is essential for user trust and safeguarding intellectual property. Current privacy-preserving diffusion models rely on fully homomorphic encryption (FHE), which is time-intensive and can compromise image quality. We introduce Anonymous-Diffusion, a diffusion as a service (DAAS) framework. This framework maintains privacy without using FHE by exploiting the irreversible nature of neural network layers and the characteristic that predicted noise in the diffusion process follows a normalized Gaussian distribution. Furthermore, we ensure anonymity by Smart Contract and Blockchain. User can use this service on demand anonymously. In comparison to existing research like HE-diffusion, which incurs a 200% time overhead and noticeable quality degradation, our protocol achieves the same level of security with only a 4% time overhead and no loss in image quality. To our knowledge, this is the first solution to achieve these results without FHE while preserving high-quality image output. © 2025 IEEE.

Cross-chain communication is the cryptographic technology that sends and receives tokens or data among multiple blockchains. A parent-child blockchain system is one of the methods of cross-chain communication. Existing studies apply Byzantine fault-tolerant (BFT) mechanisms to achieve consensus among multiple N blockchains. However, cross-chain communication based on PBFT requires O(N2) communication complexity to achieve consensus mechanisms among multiple N blockchains (no scalability). In addition, existing cross-chain communication do not propose a scheme to prevent a malicious act by users who mediate tokens (no security). Furthermore, there is no existing scheme that simultaneously satisfies unforgeability, which prevents the sender of a token from forging the sent token, and unlinkability, which protects the privacy of the sender and receiver (no unlinkability and no unforgeability). In this paper, we propose a Privacy-Preserving Scalable Cross-Chain Communication (PPSCCC) among multiple blockchains that solve the above problems. In PPSCCC, by applying the commitment scheme to the token mediators, the communication complexity among blockchains for consensus mechanism can be achieved with O(N). We also prove unlinkability and unforgeability by using the hiding and binding properties of the commitment scheme. In addition, by introducing Proof of Commitment Reputation (PoCR), we achieve a scheme to prevent malicious behavior of users who mediate tokens. We then implement our proposed scheme. Finally, we compare our scheme with existing schemes to highlight the strengths of our scheme and show that our scheme is more practical. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

Text-to-image generation is trending in the generative AI field. Stable Diffusion is the state-of-the-art among open-source projects. Many artists and service providers customize the diffusion model for special textures. However, there is no protection for the privacy of the user's input text prompt, output image, and the customized model on the server. Privacy is crucial for user trust and protecting intellectual property. Existing privacy-preserving diffusion models use fully homomorphic encryption (FHE), which is time-consuming and can degrade image quality. We propose Privacy-Diffusion, a framework that preserves privacy without FHE by leveraging the irreversible properties of neural network layers and the property that in the diffusion process, the predicted noise is a normalized Gaussian distribution. Our framework protects clients' input text prompts and generated images from the server and safeguards customized models from clients. Compared with existing research HE-diffusion which spent 200% extra time and visible quality loss, our protocol can reach the same security level with only 4% extra time and has no quality loss. To our knowledge, we are the first to achieve this goal without FHE while maintaining high-quality image output. © 2025 IEEE.

Text-to-image generation is trending in the generative artificial intelligence (GenAI) field. Among open-sourced image generation projects, Stable Diffusion is the state-of-the-art. Many artists and service providers customize the diffusion model to generate featured high-quality images. However, there is no protection to the privacy of the input text prompt, output image, and customized model. Privacy is very important since it can increase users' willingness to use the service and protect the service provider's intellectual property. Existing privacy-preserving diffusion model require fully homomorphic encryption (FHE) to ensure its privacy and security. Nonetheless, FHE is very time-consuming and may reduce accuracy due to approximations and deteriorate image quality. In this research, we propose Privacy-Diffusion, a privacy-preserving diffusion framework without FHE. By utilizing the irreversible property of neural network layers and the property that the predicted noise in the diffusion process is a normalized Gaussian distribution. Our framework can be applied to all kinds of diffusion models to protect clients' input text prompt and the generated image from being learned by the server, as well as customized models from being learned by the clients. Our protocol is secure and efficient. Compared with existing research, HE-diffusion, which spent 200% extra time and visible quality loss, our protocol can reach the same security level with only 19% extra time and has no quality loss. To the best of our knowledge, our Privacy-Diffusion is the first protocol that achieves this goal without using FHE and maintain the same high-quality image output as the original model.

Commitment schemes are cryptographic schemes that can be applied to zero-knowledge proof construction and blockchain construction. Recently, lattice-based cryptography has been intensively investigated due to the promising potential in quantum cryptography. Accordingly, commitment schemes based on lattice assumptions have been studied for practical applications. Notably, applications often require committing an arbitrary message with low communication costs, so commitment schemes must be satisfied with fewer length restrictions and fewer extensions to the messages. Several studies have been conducted to achieve the problem, including the study published by Baum et al. in 2018. However, the scheme in question still utilizes the message domain for extraneous purposes. We design a length-extension-free commitment scheme ComMWM in which the length of the message string is large relative to the length of the commitment string, improving on the commitment scheme of Baum et al. Furthermore, we prove that the hiding and binding properties of ComMWM are based on the hardness of the decisional search knapsack problem and extended search knapsack problems, respectively. Finally, we evaluate the computation costs of generating commitment value between ours and Baum et al.’s commitment scheme. Authors

Cross-chain communication is a cryptographic technology that allows communication between different blockchains. Cross-chain communication is a technology that is gaining attention to expand the potential of blockchains, but it is difficult to construct securely and efficiently. The construction of the M+1st-price sealed bid auction in cross-chain communication is also one of the difficult schemes to realize. The existing M+1st-price sealed bid auction scheme uses a commitment scheme that needs to open the winner's bid amount after the auction is finished, which does not protect privacy. Furthermore, it is inefficient to construct it using the existing commitment scheme since there are multiple blockchain participants as bidders in cross-chain communication. In this paper, we propose a privacy-preserving and efficient M+1st-price sealed bid auction in cross-chain communication. To achieve privacy-preserving, we propose a scheme that can determine the maximum bid amount without disclosing the largest bidder using a vector commitment scheme. Furthermore, the vector commitment scheme allows the verifier to aggregate multiple bid values into one commitment. Thus, efficiency can be achieved by aggregating the multiplier commitment value by using a vector commitment scheme. Also, by combining the vector commitment scheme and encryption scheme, only the verifier can compare the bid value. Finally, we compare our scheme and other existing schemes to emphasize the contribution of our scheme. © 2024 IEEE.