Protecting Healthcare Data Privacy in Nigeria with Homomorphic Encryption

[mahmudsudo]

Zama Grant Program: Application

Please give us as much information as possible on the project you would like to submit. You can find inspiration from our existing list of grants.

• Library targeted: Concrete

• Overview:
• This project aims to develop a secure and efficient system for processing healthcare data in Nigeria while preserving patient privacy. The project will leverage Zama's open-source homomorphic encryption (FHE) tools to build a platform that allows authorized personnel (doctors, researchers) to perform critical computations on encrypted medical data without needing to decrypt it.

• Description:
• This project aims to develop a secure and efficient system for processing healthcare data in Nigeria while preserving patient privacy. The project will leverage Zama's open-source homomorphic encryption (FHE) tools to build a platform that allows authorized personnel (doctors, researchers) to perform critical computations on encrypted medical data without needing to decrypt it.

Benefits:

Improved patient privacy: By keeping medical data encrypted, the project reduces the risk of data breaches and unauthorized access.
Enhanced healthcare research: Researchers can analyze large datasets of encrypted medical information to identify trends, develop new treatments, and improve public health outcomes in Nigeria.
Increased trust in healthcare systems: Patients will be more likely to share their data if they know it's securely protected.
Technical Approach:

Utilize Zama's FHE libraries like Concrete to build a platform for encrypting and performing computations on sensitive healthcare data (e.g., patient demographics, diagnoses, treatment records).
Partner with local hospitals and research institutions in Lagos to gather real-world data and test the effectiveness of the system.
Develop user-friendly interfaces for authorized personnel to easily interact with the encrypted data.

• Reward:
• 20,000 USD

• Related links and reference:
• This is an idea in incubation

[zama-bot]

Hello mahmudsudo,

Thank you for your Grant application! Our team will review and add comments in your issue! In the meantime:

1. Join the FHE.org discord server for any questions (pick the Zama library channel you will use).
2. Ask questions privately: [email protected].

[aquint-zama]

Hello @mahmudsudo,

Could you provide more information on the type of computation performed on encrypted data and on the data you will use to develop your platform.

Regarding reward, we will suggest one when we will have more info, do you have an estimated workload for your Grant?

[mahmudsudo]

Data Computations with Homomorphic Encryption (FHE):

Our project leverages FHE's ability to perform specific computations on encrypted healthcare data. This ensures patient privacy throughout the analysis. Here are the key functionalities we aim to achieve:

Essential arithmetic operations: We will focus on enabling addition, subtraction, multiplication, and division on encrypted data. This allows calculations relevant to our chosen use case, such as:
Determining patient age based on encrypted birthdate.
Calculating medication dosage based on encrypted weight.
Encrypted comparisons: This enables comparing encrypted data points to identify similar records or patients within a specific criteria, such as age range.
Statistical analysis on encrypted data: We will utilize FHE to compute statistical measures like averages, medians, and frequencies. This allows researchers to analyze trends without revealing individual details.
Data Selection for Platform Development:

To ensure project feasibility and address data privacy concerns, we propose the following approach:

Specific Use Case: We will initially target a well-defined area within healthcare data processing.
Examples: Analyzing patient demographics for public health research or evaluating treatment response rates for a specific disease.
Data Partnering: We will collaborate with local hospitals and research institutions to obtain anonymized and aggregated datasets relevant to the chosen use case.
This ensures data adheres to all data privacy regulations in Nigeria.
Illustrative Example:

Use Case: Analyze the effectiveness of a new cancer treatment using historical patient data.
Data Required: Encrypted datasets containing:
Patient demographics (age, gender)
Diagnosis information (cancer type, stage)
Treatment details (medication type, dosage)
Computations:
Calculate overall patient survival rates after the new treatment compared to traditional methods.
Identify factors influencing treatment response (age, specific mutations) while maintaining data privacy.
Addressing FHE Limitations:

We acknowledge the current limitations of FHE:

Performance Overhead: FHE computations might be slower than traditional methods due to the encryption process.
Limited Functionality: Not all mathematical functions can be efficiently performed with current FHE schemes.
Our approach to mitigate these limitations:

Algorithm Optimization: We will utilize efficient FHE libraries like Zama's Concrete to minimize performance overhead.
Focusing on Crucial Computations: We will prioritize calculations essential for the chosen use case and acknowledge limitations for more complex operations.
By carefully selecting the type of computations and data relevant to our chosen use case, we aim to demonstrate a realistic and achievable project within the grant proposal. This ensures we address patient privacy concerns while leveraging FHE's potential for secure healthcare data analysis.

[mahmudsudo]

As regards the reward , we have an estimated feasible workload that has been extensibly researched .

[aquint-zama]

Hello @mahmudsudo,

Thanks for your grant proposal. Indeed, health application critically need privacy, we agree with that and we have already seen use-cases or companies being built around that.

We're sorry to inform you we are refusing your grant proposal, because of lack of clarity:

• it's not clear what model you'll build with Concrete, where you'll find the dataset etc
• it's not clear how you will "collaborate with local hospitals and research institutions to obtain anonymized and aggregated datasets relevant to the chosen use case"; do you already have these contacts?
• it's not clear what will be shown as deliverable at the end

Furthermore, 20k$ looks a lot, especially when things are vague. Maybe better to start with a small & precise grant, and when we're happy with the outcomes, we can see larger.