Medical Data Leakage Is Serious. How Can Blockchain Protect Privacy?

Thanks to the continuous improvement of information technology and bioscience, the amount of data stored in the medical industry has increased explosively. The huge medical data resources provide great help for the rapid development of human medical and health research, especially in disease prevention and control. At the same time, medical data leakage caused by system failure, human error, and malicious attack has also brought huge losses to society.

In the e Internet era, people generate new data almost every minute. In recent years, frequent data leakage events have drawn attention to the right of data privacy, because people have realized that someone is making profits from massive data. In September 2011, Forrester, an independent research organization, released a report on personal identity management. The report said that the total amount of third-party personal privacy data transactions in the U.S. market had exceeded $2 billion. According to a 2020 data leakage report released by IBM, the average cost of data leakage events from August 2019 to April 2020 was as much as $3.86 million;of that,the healthcare industry ranks first in average leakage cost for the tenth consecutive year with $7.13 million.

Blockchain technology is the most effective way to solve the problem of data privacy through federated sharing. Through a series of technologies such as trusted hardware environments, homomorphic encryption, secure multi-party computation, and federated learning, multi-party federated computation is realized without leaving the location; data sharing incentives and automatic benefit distribution are realized through node governance and smart contracts in the blockchain. Based on a combination of the above two, GDF (Good Data Foundation) launched a data protection and sharing solution — the GoodData blockchain.

In order to make data available but invisible, GoodData has developed a new privacy protection federated learning framework based on MPC-SS (MPC with Secret Sharing) — the GoodData ML SDK.

Federated learning enables mobile devices to learn shared prediction models cooperatively and save all training data on the device, which has got rid of the dependence of central server in standard machine learning. The GoodData ML SDK extends this framework beyond mobile devices and applies it to different data owners to cooperate and train better machine learning models. Combined with blockchain, privacy preservation algorithm, data preprocess, vertical federated learning, horizontal federated learning, and other technologies, GoodData ML SDK realizes the following functions: it is open and transparent to customers and data owners in the whole training and prediction process; it protects the private data, and avoids disclosing any personal information during training and prediction; Supports most famous machine learning and depth learning algorithms in vertical and horizontal settings; and it handles different data types and formats across different data owners.

Through private computation services and the incentive mechanisms of the GoodData blockchain, everyone can obtain a certain amount of tokens by sharing their personal data in a privacy-protected environment, and this data will be used for encrypted computation by users with data computing needs. For example, the medical and health industry conducts scientific research on users’ sleep data. Data is a double-edged sword, and rational utilization will be a treasure for mankind. GDF believes that with the help of the GoodData blockchain, data will bring a clearer future to the medical industry.

Summary of GoodData’s official account

WeChat subscription account：Good Data Foundation

Medium：https://medium.com/gooddatafound

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