Privacy-preserving Digital Health

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. Private Health: Securing Genetics with the Enya SDK

We help healthcare companies to secure sensitive data. For example, consider a digital health app to manage cardivascular health. Using secure multiparty computation, data entered by the patient in response to standard risk assessment questions (e.g. smoking status, diabetes, BMI) can be analyzed without the raw data ever leaving the patient's phone. This protects that patient's privacy, but also protects healthcare providers, since they never see or handle sensitive patient data that would expose them to liability if these data were lost or leaked.

3. How does this work?

First, specify your algorithm's coefficients and then call our API to perform linear operations 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 ))

Visit Get Started to learn more, and contact us at [email protected] to start building secure analytics tools for fintech, insurance, healthcare, and other sensitive verticals.