litholight / healthinsighter Goto Github PK

Objective:

To design and implement a secure user authentication system for the HealthInsighter application, enabling both registration and login functionalities. This system should support future scalability to include roles and permissions for different types of users.

Requirements:

• Secure Authentication Mechanism: Utilize secure practices for password handling, including hashing and salting.
• User Registration: Allow new users to register, capturing essential information such as email, password, and any additional relevant details.
• Login Functionality: Enable users to log in with their credentials, with error handling for incorrect inputs.
• Session Management: Implement session management to maintain user state between different requests.
• Password Recovery: Provide a mechanism for users to recover or reset their passwords.
• Extendibility for Social Logins: Structure the authentication system to allow easy integration of social login options (e.g., Google, Facebook) in the future.
• Integration with .NET Identity: Leverage Microsoft.AspNetCore.Identity for managing users, roles, and authentication.

Tasks:

• Research and select appropriate authentication frameworks and libraries compatible with .NET 8.0.
• Define the user model and any additional data structures needed for authentication.
• Implement the registration backend logic with input validation and secure password storage.
• Develop the login mechanism, including session management and security considerations.
• Create user interface forms for registration and login.
• Implement server-side validation and error handling for the authentication process.
• Test the authentication flows for security vulnerabilities and functionality.
• Document the authentication setup and usage instructions.

Discussion Points:

• Considerations for GDPR compliance and user data privacy.
• Potential need for two-factor authentication (2FA) as an additional security layer.
• Strategies for preventing common security threats (e.g., SQL injection, cross-site scripting).

Additional Context:

User authentication is a foundational aspect of the HealthInsighter application, laying the groundwork for personalized health data management and insights. It's crucial that the authentication system is robust, secure, and flexible to support the application's future growth and potential multi-user capabilities.

Objective

Design and implement foundational data models for the HealthInsighter application, supporting comprehensive handling, storage, retrieval, and analysis of diverse health data types.

Models to Implement

The application will initially concentrate on the following data models to accommodate a wide array of health data types:

1. HealthRecord: Captures routine health metrics.

   • HealthRecordId: Unique identifier.
   • UserId: Associates the record with a specific user for scalability and personalization.
   • RecordType: Identifies the type of health data (e.g., heart rate, steps).
   • Timestamp: The date and time when the data was recorded.
   • Value: Flexible to support various data types.
   • Unit: Specifies the measurement unit (e.g., BPM, steps).
   • DataType: Categorizes the nature of the data (numeric, text, complex).

2. HealthEventType: Classifies different types of health events or metrics.

   • EventTypeId: Unique identifier.
   • Name: The descriptive name of the event.
   • Description: A detailed explanation of the event.

3. HealthEvent: Logs instances of health events.

   • HealthEventId: Unique identifier.
   • EventTypeId: References the HealthEventType.
   • UserId: Links the event to a user.
   • Timestamp: When the event occurred.
   • Notes: Additional details about the event.

4. MeasurementType: Defines criteria for various health measurements.

   • MeasurementTypeId: Unique identifier.
   • Name: Name of the measurement.
   • Unit: Unit of measurement.

5. BloodSample: Stores detailed clinical data from blood analysis.

   • BloodSampleId: Unique identifier.
   • UserId: Associates the sample with a user.
   • Timestamp: The collection time of the blood sample.
   • Metrics: Complex data type (e.g., JSON) storing various metrics from the analysis.

6. AnalysisResult: Contains the outcomes of health data analysis.

   • AnalysisResultId: Unique identifier.
   • UserId: Links the analysis result to a user.
   • Timestamp: When the analysis was completed.
   • ResultType: The type of analysis conducted.
   • Value: The outcome or insight derived from the analysis.
   • Notes: Interpretations or additional notes about the result.

Tasks

• Implement the data models in the HealthInsighter.Core project, ensuring they align with .NET best practices for data modeling.
• Design the models to be flexible, allowing for the integration of future health data types and facilitating complex data analysis techniques.
• Ensure the models support efficient storage, retrieval, and querying of health data, from basic metrics to complex clinical information.

Discussion Points

• Develop efficient strategies for processing and storing diverse health data types.
• Address potential scalability challenges, particularly with user data growth and multi-user support.
• Explore optimized approaches for managing complex data types, such as BloodSample, and ensuring the architecture supports in-depth analysis and discovery of health insights.