Privacy Framework for Healthcare

Hospitals and health-tech companies need an end-to-end privacy framework to collaborate on sensitive patient data

Build Secured Data Partnerships
Collaborate on sensitive patient data with the peace of mind that patient privacy is always secured.
Build Trust with Patients
Earn the trust of patients with robust privacy guarantees.
Deliver Best-in-Class Care
Leverage the latest advances in data science to deliver patient care without worrying about data leaks.

1. Secure computation: Disease Risk Estimation

With recent advances in human health and genetics, people have unprecedented access to information about their health risks and possible interventions. Computing these diagnoses and recommendations requires data spanning genetics, physiology, and lifestyle but most of us rightly think twice about sharing sensitive information. At first sight this seems like an insurmountable problem – how to balance better medical care with privacy? Recent advances in computer science, cryptography, and security research offer a way forward.

2. The Blockdoc App: Securing Genetics with the Enya SDK

Working with Rainbow Genomics, we developed a technology demonstrator iOS and Android app that uses the Enya SDK and API. The Blockdoc App allows patients to estimate their risk of cardiovascular events using secure multiparty computation. In this approach, the data entered by the patient in response to standard risk assessment questions (e.g. smoking status, diabetes, BMI) never leave their phone, but can still be computed on by Rainbow algorithms. This protects that patient's privacy, but also protects Rainbow, since they never see or handle sensitive patient data that would expose them to liability if these data were lost or leaked. See the Blockdoc App page for more information.

3. How does this work?

You need to specify your algorithm's coefficients and then you can securely perform a linear operation on user data, for example:

import * as EnyaSMC from 'enyasmc';

/* The user's sensitive medical inputs */
data = {
  birthyear: 1950, 
  gender: 1, 
  height: 170, 
  weight: 50, 
  smoking: 1, 
  diabetes: 1, 
  hdlc: 3, 
  cholesterol: 3, 
  bp: 3

export const secureCompute = (data) => async (dispatch) => {
  //----------------- User's input ---------------------
  EnyaSMC.Input.apply(this, Object.values(data))

  //-------------- Configure settings ------------------
      algo_name: "MySecretAlgorithm",

  //-------------- Run the model ------------------------
  const model = await EnyaSMC.Linear()

  //-------------- Get the result -----------------------
  if (model.status_code == 200) { 
    result = parseFloat(model.secure_result).toFixed(2); 
  //------------ Dispatch the result --------------------
  let updatedSMC = { result };
  dispatch( secureComputeSuccess( updatedSMC ))

Download the app from our GitHub repository to start building secure healthcare and clinical research apps. Visit Get Started to learn more.

Download Sample App