## 3 Materials and Methods
### Security Analysis  
Ensuring data privacy and security is critical in multimodal data processing, particularly in fields such as financial forecasting, medical imaging, and autonomous driving, where data breaches can lead to severe financial losses or legal risks. The proposed sliding-window computation network and sliding loss function are designed not only to optimize multimodal data modeling but also to integrate multiple security-enhancing mechanisms to ensure privacy protection and computational security. The security guarantees of this framework can be categorized as follows. First, local data processing and decentralized computation are adopted. The sliding-window computation network ensures that data processing occurs locally, eliminating the need to transmit raw data, thereby reducing the risk of data exposure. Compared to traditional centralized data processing methods, this approach significantly mitigates the possibility of cloud-based data breaches. In decentralized computing scenarios, only necessary encrypted computation results are shared among collaborators, ensuring privacy while still achieving efficient training and inference. Second, trusted execution environments (TEE-based computing) enhance cloud-based security. In cloud computing environments, data are often processed remotely, making it challenging to guarantee data confidentiality during computation. The proposed framework integrates TEE-based computing (e.g., Intel SGX) to establish secure execution environments, ensuring that, even when data are processed in the cloud, they remain encrypted and inaccessible to unauthorized parties. This approach effectively mitigates malicious server attacks while ensuring that model parameters and private data are not compromised. Additionally, differential privacy (DP) prevents information leakage. During collaborative training, attackers may attempt to reconstruct original data using gradient updates or intermediate training results. To address this, the proposed framework incorporates differential privacy, introducing controlled noise into gradient computations to prevent individual data points from being reverse-engineered. Mathematically, differential privacy ensures that, even if an attacker gains partial access to model parameters, they cannot accurately reconstruct the original dataset. Moreover, the framework allows for adjustable privacy budgets, enabling different levels of privacy protection based on application requirements.  
For cloud-based deployment, a zero-trust architecture (ZTA) is employed to enforce restricted data access, ensuring that only authorized users and devices can interact with sensitive data. Unlike traditional security models that assume implicit trust, ZTA employs multi-factor authentication (MFA), dynamic access controls, and continuous verification to prevent unauthorized access. Additionally, secure multi-party computation (SMPC) enables collaborative learning across institutions while ensuring that raw data remain confidential. This technique is particularly beneficial for privacy-sensitive federated learning tasks. To align with global privacy regulations, the proposed framework ensures strict compliance with legal standards governing data storage, access, and processing. Under the General Data Protection Regulation (GDPR), the framework adheres to the principle of data minimization as sliding-window computation only utilizes essential localized data fragments. Moreover, explainability mechanisms ensure that users can understand how their data are utilized. For healthcare applications, compliance with the Health Insurance Portability and Accountability Act (HIPAA) is maintained through encrypted storage and restricted access control. In financial data security, the framework integrates distributed ledger technology and data provenance tracking, ensuring the integrity and traceability of shared data across institutions while meeting financial sector compliance requirements.