## 3 Related Work
### Innovation  
To better contextualize the contribution of our work within the existing technological landscape, Table 2 provides a comparative overview of the limitations found in current approaches and the corresponding innovations introduced by our system. This comparison highlights how AURORA MINDS addresses critical challenges in privacy, identity management, and healthcare applicability that are not fully resolved by existing PETs, blockchain-based IdM solutions, or Federated Learning frameworks.  
In a nutshell, there is a proliferation of digital applications and frameworks designed for diagnosing ADHD. These applications may not always adhere to established diagnostic criteria, leading to the potential for misdiagnosis or overdiagnosis. These applications aim to simplify and streamline the diagnostic process. However, the key question is  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Domain** & **Limitations of Existing Work** & **AURORA MINDS Innovations** \\ \hline \multirow{4}{*}{**PETs**} & \multirow{4}{*}{\(-\) Low technological readiness \(-\) High computational overhead \(-\) Legal compliance issues \(-\) Privacy-preserving, modular architecture designed to integrate with existing healthcare systems \(-\) Engures GDPR-aligned by keeping users informed along with cryptographic mechanisms \\ \hline \multirow{4}{*}{**Identity Management**} & \multirow{4}{*}{\(-\) Centralized IdPs vulnerable to tracking and breaches \(-\) Simple point of failure \(-\) Behavioral data exposure to IdP} & \(-\) Distributed IdP architecture enables distributed identity management \(-\) Eliminates centralized control and strengthens resilience \(-\) Prevvents user tracking across services via unlinkable authentication \\ \hline \multirow{4}{*}{**Blockchain-based Identity Management**} & \multirow{4}{*}{\(-\) Reliance on third parties (e.g., cloud agents) \(-\) Lack of robust identity verification \(-\) Poor support for revocation and selective disclosure \(-\) Publicly visible metadata risks \(-\) Avoid using smart contracts \(-\) Avoids storing sensitive data on blockchain \(-\) Integrity and complexity \(-\) Risk of metadata exposure \(-\) Utilizes blockchain only for credential integrity and verification \\ \hline \multirow{4}{*}{**Smart Contract**} & \multirow{4}{*}{\(-\) Publicly visible metadata risks \(-\) Avoid using smart contracts \(-\) Avoids storing sensitive data on blockchain \(-\) Integrity and verification \(-\) Risk of metadata exposure} & \(-\) Avoid using smart contracts \(-\) Avoids storing sensitive data on blockchain \(-\) Integrity and verification \\ \hline \multirow{4}{*}{**Use of Blockchain in EHRs**} & \multirow{4}{*}{\(-\) High cost and inefficiency in storing health data on-chain \(-\) Limited scalability and block size issues} & \(-\) Utilizes blockchain only for VC \(-\) Assistance and revocation \(-\) Off-chain data is secured via advanced cryptography \\ \hline \multirow{4}{*}{**Federated Learning in Healthcare**} & \multirow{4}{*}{\(-\) Data heterogeneity \(-\) Balancing enhanced privacy with potential accuracy loss \(-\) PETs, DP, AES \(-\) Demand for computational resources} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Innovations of AURORA MINDS compared to related work.  
whether these tools are indeed making the process more efficient and accurate and how they contribute to the broader issue of user privacy. The criteria for diagnosing ADHD in a privacy-friendly manner are comprehensive and require a nuanced understanding of the individual's behavior across different contexts, something that may be challenging. AURORA MINDS not only stands out for its commitment to privacy and data control but also its engaging approach to ADHD diagnosis, making it a pioneer in the field. Table 3 illustrates how AURORA MINDS distinguishes itself from existing applications.