Homomorphic encryption (HE) is a foundational technology in privacy-enhancing cryptography, enabling computation over encrypted data. Recently, generalized HE primitives designed for multi-party applications, such as multi-party HE (MPHE), have garnered significant research interest. While constructing secure multi-party protocols from MPHE in the semi-honest model is straightforward, achieving malicious security remains challenging as it requires zero-knowledge arguments of knowledge (ZKAoKs) for MPHE ciphertexts and public keys.

With rapidly increasing drug abuse across the world, it is imperative to monitor their supply chain with sufficient transparency. Blockchain is a common solution for achieving transparency in supply chain monitoring, but it does not have sufficient throughput for large-scale supply chains. It is challenging to achieve throughput and privacy simultaneously because complex dependencies among the supply chain events and the need for aggregation both make the application of ZKP challenging. We present PrivHChain, a privacy-preserving hierarchical blockchain that preserves transaction privacy even against blockchain peers while allowing them to verify record consistencies. This is enabled by novel modeling of supply chain events which makes it possible to use novel efficient zero-knowledge protocol schemes to verify the complex dependencies. Novel aggregation techniques are proposed to enable the proof aggregation, and the proofs are used to design monitoring protocols. PrivHChain is implemented and validated with extensive experiments and simulations. The results indicate that (i) the extra overhead of encryption and ZKP schemes is acceptable or negligible, and (ii) the throughput is improved by up to 5 times in simulations even with all the encryption/ZKP schemes.

Online data trading is increasingly prevalent as data are becoming valuable assets. In most common conventional data trading scenarios, three parties (seller, broker, and buyer) exist, and fairness in trading is essential. This paper discusses and solves the fairness problem in two aspects. First, we considerexchange fairness, which requires payments and data exchanged correctly between buyers and the broker. In existing solutions, keys of encrypted data are traded. However, these solutions failed to provide a complete and secure design for validating keys' correctness unless they used generic theoretical but expensive methods, e.g., zk-SNARK. We address this security issue by designing a new key verification mechanism. We also present a novel atomic exchange protocol based on Hashed Timelock Contracts on Ethereum, reducing gas consumption compared to the existing approach. Second, we considerdistribution fairness, which requires correctly splitting income between the broker and sellers. Straightforward solutions are impractical, i.e., sellers participating in every transaction or traversing the blockchain. Therefore, we design a verifiable statement protocol for sellers to verify the income split efficiently. Further, analysis and experimental results indicate that extra fairness properties are securely achieved, and our protocol reduces users' on-chain participation compared to state-of-the-art protocols.

An inner product (IP) argument is a proof system that convinces the verifier of an IP relation between committed integer vectors. IP arguments are crucial building blocks for range proof and zero knowledge arguments, which can be applied to verifiable computation, confidential transactions, decentralized identification, and so on. This paper proposes a novel efficient IP argument with a trustless setup. For integer vectors of size N, the proposed IP argument provides a proof size of O(log2 N), a verification cost of O(√N), and a size of public parameter size of O(√N). The construction uses bilinear pairings and its security relies solely on the discrete logarithm (DL) assumption, a well-established standard cryptographic assumption. Consequently, we obtain the first DL-based IP argument with a trustless setup that achieves a sublinear verifier and logarithmic proof size, which we call Leopard. Furthermore, We empirically evaluate the performance of Leopard. The experimental results demonstrate that Leopard is highly efficient and scalable compared to previous works.