# Blockchain-Enabled Privacy-Preserving Ecosystem for DNA Sequence Sharing
## 4 Evaluation
### Comparative Analysis with Existing Models  
Here, we compare the performance and security of our blockchain-based ecosystem with prior genomic data-sharing models. Traditional blockchain solutions for genomic data (e.g., those in [8; 9; 11; 16]) often face scalability issues due to using a single public blockchain for all operations. For example, the ConsentChain system [16] built on Ethereum must contend with network congestion and transaction fees, limiting its throughput and cost efficiency for large-scale use [17]. In contrast, our dual-chain architecture leverages a permissioned Hyperledger Fabric network for data sharing, allowing our DNA chain to achieve dedicated throughput of around 10 transactions/s without gas fees or miner delays. This represents an improvement in performance and scalability, as our private chain can handle frequent access updates internally, whereas an Ethereum-based approach would be constrained by the public chain's ~15 TPS global limit and incurrence of costs for every transaction [18]. In terms of access control granularity, previous frameworks typically manage consent or access at the study or record level--for instance, the DWARNA biochking consent system [10] treats consent as a binary setting (broad consent yes/no), and ConsentChain allows patients to grant or revoke data access, primarily at the dataset level [17]. Our system advances this by enabling segment-level access control, where patients can specify permissions for individual regions or types of genetic data. This fine-grained approach is  
Figure 12: Throughput in decentralized identity chain. Figure 13: Throughput in DNA sharing chain. particularly important for genomic data, as it allows the sharing of only relevant genetic markers with researchers (e.g., specific genes related to a study) while keeping more sensitive segments private. Such granularity is not addressed by ConsentChain or DWARNA, which focus on whole-record consent management. With respect to security and trust, many earlier proposals rely on public blockchain identities or pseudonymous addresses [9; 11], which, while preserving anonymity, may allow participants of uncertain trustworthiness. Our model explicitly introduces a consortium governance aspect: only government-certified research institutions can join the network, and patients are verified through national ID mechanisms. This design choice reduces the risk of malicious actors compared to open networks, at the cost of requiring a trusted authority to onboard users. We believe this trade-off is suitable for medical data contexts where a level of institutional oversight is expected. Notably, the integration of a decentralized identity (DID) chain (based on [6]) in our work is a distinguishing factor. The physiological chain system [6] demonstrated the viability of self-sovereign identities in healthcare; our work builds on this by adding a second blockchain dedicated to data transactions, thereby isolating identity management from data exchange. This two-chain architecture improves upon [6] by preventing heavy data loads from impacting identity ledger performance and by providing specialized optimization (Fabric's endorsement policies) for data access workflows. We also compare our approach to the concept of using blockchain with marketplaces or incentives, as discussed in some recent works. While systems proposing tokenization (e.g., via NFTs) aim to encourage data sharing by monetization, they raise complex ethical and privacy questions (such as valuing personal genomic data). Our ecosystem takes a different path by incentivizing participation through strict privacy guarantees and trust (via government oversight) rather than direct payments. This aligns with the view that participants are more willing to share data when they trust the platform's security and governance, as opposed to purely financial incentives [18].