## 2 State-of-the-Art
### Matchmaking Solutions Using AI
The authors formally define DDAC with atomicity, consensus, and confidentiality (ACC) constraints:  
* _Atomicity_ implies that a transaction is either discarded or applied to the nodes of all allowed participants and that write operations included in such a transaction must be atomic. * _Consensus_ implies that data owners may veto a transaction involving their data with a single vote. * _Confidentiality_ implies that participants have read access to data if and only if they are included in the corresponding read-allowed group. Following this definition, the authors implement a DDAC framework using Hyperledger Fabric. ACC modules are embedded within this framework as access control managers. The framework also employs encryption modules and ledger partitioning techniques to further control data access based on attribute-based access control (ABAC) rules. In [23], the authors present a blockchain-based role-based access control (B-RBAC) mechanism for data sharing within federated systems. The authors focus on medical data and IoT environments. The access control mechanism is based on smart contracts, and the required access control policies are stored as key/value pairs through these smart contracts. The key represents data's security attribute level, and the value describes the corresponding set of conditions that must be matched to grant access. The authors further rely on the concept of \"colored coins\" to define security attribute tokens, which are then used to assess different user permissions and data access layers. Distributed access control decisions are performed through automatic executions of smart contracts. The solution also has the much-required advantage of supplying traceability information alongside solving RBAC in distributed federated environments. The prototype system is developed using HyperLedger Fabric. Data privacy and security are the focus of [24]. The authors address the issues raised by multidimensional data aggregation and access control in cloud-based IoT scenarios. Data collected from different smart things are encrypted using EBGN homomorphic encryption before being aggregated and pushed into the blockchain. Furthermore, different data access attributes are handled through ciphertext-policy attribute-based encryption (CP-ABE): the EBGN private keys for each data dimension are encrypted using the CP-ABE algorithm. This ensures that authorized parties are able to access only the corresponding required data. The authors propose the use of a trusted authority to handle key management and renewal. Access renewal is ensured through the regeneration of EBGN's public and private keys for each affected data dimension. In [25], the authors are primarily focusing on the development of a new blockchain-based protocol for secure data sharing and access. The article emphasizes the growing interest in decentralized storage solutions, particularly those based on blockchain technology. Blockchain's inherent features, such as immutability, transparency, and decentralization, offer potential advantages in addressing the limitations of centralized systems. The paper utilizes the InterPlanetary file system (IPFS) to store large files. IPFS is a peer-to-peerdistributed file system that provides content addressing and versioning. The authors acknowledge that blockchain is not suitable for storing large amounts of data and leverages IPFS to complement the blockchain's capabilities. The reference text emphasizes the importance of encrypting files before they are added to the IPFS to ensure data confidentiality. The authors state that \"the files should be encrypted before added to IPFS to ensure data confidentiality.\" However, the paper does not delve into the specifics of the encryption approach, such as the encryption algorithms or key management schemes employed. The primary focus of the paper is on the blockchain-based protocol for secure data access and collaboration rather than the intricacies of the encryption process itself.