Ensuring fairness in blockchain-based data trading presents significant challenges, as the transparency of blockchain can expose sensitive details and compromise fairness. Fairness ensures that the seller receives payment only if they provide the correct data, and the buyer gains access to the data only after making the payment. Existing approaches face limitations in efficiency, particularly when applied to large-scale data. Moreover, preserving privacy has also been a significant challenge in blockchain. In this paper, we introduce zkMarket, a privacy-preserving fair trade system on the blockchain. We ensure fairness by integrating encryption with zk-SNARKs, enabling verifiable proofs for fair trading. However, applying zk-SNARKs directly can be computationally expensive for the prover. To address this, we improve efficiency by leveraging our novel matrix-formed PRG (MatPRG) and commit-and-prove SNARK (CP-SNARK), making the data registration process more concise and significantly reducing the seller's proving time. To ensure transaction privacy, zkMarket is built upon an anonymous transfer protocol. Experimental results demonstrate that zkMarket significantly reduces the computational overhead associated with traditional blockchain solutions while maintaining robust security and privacy. Specifically, our evaluation quantifies this high efficiency: the seller can register 1 MB of data in 2.8 seconds, the buyer can generate the trade transaction in 0.2 seconds, and the seller can finalize the trade within 0.4 seconds.

Cryptographic accumulators are a common solution to proving information about a large set S. They allow one to compute a short digest of S and short certificates of some of its basic properties, notably membership of an element. Accumulators also allow one to track set updates: a new accumulator is obtained by inserting/deleting a given element. In this work we consider the problem of generating membership and update proofs for \em batches of elements so that we can succinctly prove additional properties of the elements (i.e., proofs are of constant size regardless of the batch size), and we can preserve privacy. Solving this problem would allow obtaining blockchain systems with improved privacy and scalability.

As surveillance systems are popular, the privacy of the recorded video becomes more important. On the other hand, the authenticity of video images should be guaranteed when used as evidence in court. It is challenging to satisfy both (personal) privacy and authenticity of a video simultaneously, since the privacy requires modifications (e.g., partial deletions) of an original video image while the authenticity does not allow any modifications of the original image. This paper proposes a novel method to convert an encryption scheme to support partial decryption with a constant number of keys and construct a privacy-aware authentication scheme by combining with a signature scheme. The security of our proposed scheme is implied by the security of the underlying encryption and signature schemes. Experimental results show that the proposed scheme can handle the UHD video stream with more than 17 fps on a real embedded system, which validates the practicality of the proposed scheme.