In current Data-as-a-Service (DaaS) platforms, data marketplaces are the primary mechanism by which data owners deliver data products and services to users. However, data owners incur substantial costs to collect or produce data before selling it, exposing them to economic risks due to market price fluctuations. Options trading, used in traditional commodity markets, can alleviate sellers' financial burdens with forward contracts at fixed option prices and pre-paid option premiums. To our knowledge, the application of option trading in data marketplaces has not been thoroughly studied or implemented. In this work, we advocate for the first data options marketplace on a DaaS platform that mitigates price fluctuation risks and reduces market entry barriers. Designing such a marketplace involves several key challenges, including the easy replication of data and the difficulty in assessing data quality before production. To address these challenges, we first design a data quality prediction method based on sellers' reputation. Following this, we model and quantify the competitive relationships among buyers. On this basis, we model the interactions between sellers and buyers in the data options market as a two-stage Stackelberg game, focusing on maximizing sellers' profit. We formally derive the perfect subgame equilibrium for option trading and derive each seller's optimal pricing strategy. Numerical experiments demonstrate the superiority of data options trading over conventional data trading methods in DaaS platforms.

Blockchain provides trustworthy properties such as decentralization, traceability, and transparency, and has been applied in various fields. Given the complexity of guaranteeing the accuracy of the input data, erroneous data may be stored on the blockchain, making it hard to modify. As an effective solution, redactable blockchain allows on-chain data to be modified by introducing a chameleon hash function with a trapdoor, which enables on-chain erroneous data to be corrected. However, existing redactable blockchain solutions often require trusted third parties, have huge overhead, or lack effective accountability mechanisms to meet data security needs. Therefore, this paper proposes a secure, efficient and accountable data management scheme based on redactable blockchain. To cope with the possible frequent editing needs, we design an efficient and accountable distributed trapdoor recovery mechanism that reliably maintains data security while avoiding the security risks associated with centralized management. Various information during the trapdoor management process is also recorded on the chain as the basis for implementing accountability mechanisms. In addition, the one-time trapdoor technology allows the blockchain system to reduce the maintenance complexity of the system by eliminating the need to frequently update the system parameters when partial trapdoor information needs to be published. Theoretical and experimental results show that our scheme can achieve an efficient accountability mechanism while modifying the data on the chain at low cost.